Software and Projects Using KIM

OpenKIM’s primary objectives are to advance materials research by serving as a trusted, curated source of interatomic potentials and by enabling automated, high-throughput computational workflows. Its central role is evidenced by the widespread adoption of the KIM API across a diverse ecosystem of atomistic simulation codes and tools, extensive recognition in review literature, and broad use of OpenKIM potentials in scientific applications.

Below is a summary of OpenKIM usage illustrating its impact.


Atomistic and multiscale simulation codes

  • Image - As Soon As Possible (ASAP) is a calculator for doing large-scale classical molecular dynamics within the Atomic Simulation Environment (ASE). ASAP has full support for KIM Portable Models (KIM API v2) as of version 3.11.3. The ASAP manual can be found here and installation instructions here.

  • Image – The Atomic Simulation Environment (ASE) is a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations. ASE supports various simulators using "calculators" that provide an interface to these codes. A KIM calculator for running Portable Models and Simulator Models (for simulators that have an ASE calculator) is available in ASE as of version 3.19.0. See here for instructions and examples.

  • Image – DL_POLY is a general purpose classical molecular dynamics simulation software developed at Daresbury Laboratory in the UK. DL_POLY has support for KIM API v2 (released Sep 2000). For more information on DL_POLY and to obtain the code, visit the DL_POLY website.

  • Image – exaStamp is a high-performance molecular dynamics simulation package built on the onika/exaNBody framework. Its primary focus is microscopic-scale material modeling under extreme conditions, with particular strengths in high strain rates, shock physics, and small-scale mechanic. exaStamp has support for KIM API v2, allowing seamless access to the extensive library of KIM-compliant interatomic potentials. General guidelines for building exaStamp can be found in the related documentation with additional instructions to get KIM support.

  • Image – GULP is a molecular simulation program emphasizing analytical solutions based on lattice dynamics, but also including a molecular dynamics mode. GULP provides full support for KIM Portable Models as of version 4.2. To use KIM with GULP you must add the flag -DKIM to DEFS in getmachine so that the code that supports KIM is enabled during compilation. Models are then specified using the kim\_model option. At present only a single type of each element is supported when using KIM and so species types are ignored when being passed to KIM models.

  • Image – ITAP Molecular Dynamics (IMD) is a software package for classical molecular dynamics simulations, originally developed by the late Jörg Stadler within the framework of Project C14 at the Institute for Theoretical and Applied Physics (ITAP) at the University of Stuttgart. Johannes Roth, alongside other members of the ITAP group, has been involved in the ongoing development and application of this software, which was primarily used for studies in condensed matter physics.

  • Image – The molecular dynamics program LAMMPS has full support for the KIM API v2 as of 28 Feb 2019. This support is implemented in the KIM package in LAMMPS. See the general instructions for building LAMMPS and specific instructions for building LAMMPS with KIM support. To run LAMMPS with a KIM interatomic potential, install it (see instructions on how to do this on the Obtaining KIM Models page). Then follow the instructions in the LAMMPS documentation on how to use KIM Models in LAMMPS.

  • Image – QUIP (QUantum mechanics and Interatomic Potentials is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and includes support for OpenKIM models, as well as tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to various software platforms.

  • Image – Molly.jl is an open-source Julia package designed for molecular dynamics (MD) and physical system simulations, written entirely in Julia. It enables simulating atomic interactions with a focus on differentiable molecular simulation (allowing automatic differentiation), GPU acceleration (CUDA), and customizable interactions, supporting research in proteins and molecular systems. KIM usage is described in the documentation.

  • Image – The quasicontinuum (QC) method is a multiscale simulation platform in which fully-resolved atomistic regions are embedded in a coarse-grained finite element regions. A high-performance 3D version of the QC code has been developed with support for polycrystalline materials of arbitrary crystal structure (simple and complex lattices). The QC code provides full support for KIM API v2. For more information on the code and how to access it, contact the developers.


Workflow tools

  • Image – Conda packages are available for easy installation of various OpenKIM packages including: The KIM API (kim-api), automated convergence detection for computational protocols (kim-convergence), query interface to openkim.org (kim-query), and the library of OpenKIM potentials (openkim-models).

  • Image – EasyBuild packages are available for easy installation of the KIM API (kim-api) and the library of OpenKIM potentials (openkim-models).

  • Nanoworks – Nanoworks, formerly known as gpaw-tools, is a unified, high-level Python interface for conducting Density Functional Theory (DFT), Molecular Dynamics (MD), and Machine Learning (ML) potential calculations. Its mdsolve solver allows MD and optimization using OpenKIM potentials.

  • Image – The iprPy package is a computational framework supporting open source calculation methods. The framework focuses on making the barriers for usage as low as possible for both users and developers of calculations. The primary focus is on classical atomistic calculations using interatomic potentials.

  • Image – Orchestrator is general-purpose computational framework developed at LLNL for streamlining the complex workflows of building, training, and analyzing interatomic potentials alongside the execution and analysis of large scale MD simulations to answer fundamental scientific questions. Orchestrator is built upon OpenKIM infrastructure including the KIMkit Python package providing standalone hosting of interatomic potentials, and the KIM Developer Platform (KDP). For more information on KIM usage, see the documentation.

  • Image – Pyiron is an integrated development environment (IDE) for computational materials science focused on simplifying the development of simulation protocols. It enables users to quickly iterate over all KIM potentials to test and validate their simulation protocols, by leveraging the LAMMPS KIM interface. (See here for examples of using pyiron with KIM.)

  • Image – The Simulation Environment for Atomistic and Molecular Modeling (SEAMM) is a user-friendly environment for computational molecular and materials science. SEAMM is a workflow manager composed of plug-ins that wrap popular software packages and tools allowing a user to set up and run quantum and classical molecular simulations. SEAMM is developed by the Molecular Sciences Software Institute (MolSSI).


Atomistic analysis tools and projects

  • Image – Carbon Potentials is an interactive online tool for comparing carbon interatomic potentials. The tool is designed to determine the transferability of the many carbon potentials available in the literature. Each potential is used to simulate various properties of carbon and the results are presented in this tool.

  • Image – The ColabFit Exchange is an open-access online database for discovering, exploring, and contributing datasets aimed at developing data-driven interatomic potentials (DDIPs) for materials science and chemistry. ColabFit aims to create a computational framework that enables researchers to rapidly develop and deploy DDIPs for complex material systems by connecting existing cyberinfrastructure resources of first principles and experimental data with a variety of fitting frameworks, and to share developed DDIPs through OpenKIM.

  • Image – Enalos Cloud Platform developed by NovaMechanics Ltd, is an online, freely available cheminformatics and nanoinformatics cloud platform. It hosts a broad range of predictive models provided as web services, offering flexible and powerful cloud computing resources that reduce the barriers to entry for complex scientific calculations. Enalos Cloud tools using OpenKIM interatomic potentials include:
    • ASCOT is a Web tool for the digital reconstruction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors.
    • NanoConstruct is a nanoparticle construction tool.
    • NanotubeConstruct is a nanotube construction tools.

  • Image – As part of the EU SSbD4CheM project, the HydroNanoConstruct tool was deployed on the EosCloud web platform. It is a nanoparticle construction tool for hydrated metal oxides utilizing OpenKIM interatomic potentials.

  • Image – Joint Automated Repository for Various Integrated Simulations (JARVIS) leaderboard for evaluating force fields includes OpenKIM potentials.

  • Image – MDStressLab++ is a package for post-processing molecular dynamics or molecular statics results to obtain stress fields using different definitions of the atomistic stress tensor. MDStressLab++ currently supports KIM API v2. Both Fortran and C++ versions are available. For more information, contact the developers.

  • Image – nanoHUB is an open-access platform providing online simulation tools, educational resources, and data for nanotechnology, engineering, and data science. NanoHUB tools using OpenKIM interatomic potentials include:
    • LAMMPS is a NanoHub tool to run LAMMPS by uploading a data file and command script.
    • LLM for LAMMPS used OpenAI's GPT-4 to manage LAMMPS simulations including generating input scripts, running simulations, and querying the results via the thermodynamic output.
    • Minimal Molecular Simulation - Perform simple molecular dynamics and statics simulations.
    • Nanomaterials Mechanics Explorer enables users to explore properties of materials from the atomistic scale including dislocation motion, crack propagation, plastic deformation, melting, and martensitic transformation.
    • SimTool is a NanoHub tool for performing findable, accessible, interoperable, and reusable (FAIR) simulations utilizing OpenKIM interatomic potentials. This application is based on the Sim2Ls framework.

  • Image – The Virtual Fab simulation laboratory provides an interactive platform to construct, carry out, and analyze simulations pertaining to nanoscale devices, with an emphasis on semiconductors. Virtual Fab currently offers full support for the use of OpenKIM Models along with a visualization interface available for plotting and comparing KIM Test Results for Models relevant to a given application.

  • VSSR-MC – Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) is an algorithm for sampling surface reconstructions. VSSR-MC samples across both compositional and configurational spaces.


Interatomic potential development

  • 2NN-MEAM – 2NN-MEAM is based on the modified embedded atom method (MEAM) potential formalism that can be applied to a wide range of materials in different crystal structures including fcc, bcc, hcp, diamond, as well as gasses. 2NN-MEAM extends MEAM to second neighbor interactions to solve some stability problems. MEAM and 2N-MEAM parameterizations can be deployed via the OpenKIM MEAM Model Driver.

  • Image – Atomistic Machine-learning Package (AMP) is open-source package designed to easily bring machine-learning to atomistic calculations. Use of OpenKIM interatomic potentials is described in the documentation.

  • Image – The KIM-based learning-integrated fitting framework (KLIFF) is a package for fitting analytic and machine learning interatomic potentials (IPs). Trained IPs are compatible with the KIM API and can be used with other codes that are compatible with KIM (such as those listed on this page). KLIFF is written in Python with computationally intensive components implemented in C++. The code is modular by design enabling a flexible approach to fitting incorporating different components: atomic environment descriptors, functional forms, loss functions, optimizers, and quality analyzers. The package is available through GitHub with extensive documentation and examples available here. An article describing the KLIFF packages is available here.

  • PANNA – Properties from Artificial Neural Network Architectures (PANNA) is a package to train and validate machine learned interatomic potentials based on different atomic environment descriptors and atomic multilayer perceptrons. PANNA parameterizations can be deployed via the OpenKIM PANNA Model Driver.

  • Image – Potfit is a free implementation of the force-matching algorithm to generate effective potentials from ab-initio reference data. Potfit provides support for KIM API v2 as of version 20190325.

  • PolyMLP – pypolymlp is a Python code for generating Polynomial Machine Leaning Potentials (PolyMLP) based on datasets generated from density functional theory (DFT) calculations. In addition to potential development, pypolymlp allows users to compute various physical properties and perform atomistic simulations using the trained MLPs. PolyMLP parameterizations can be deployed via the OpenKIM PolyMLP Model Driver.


Review papers

This is a partial list of review papers that acknowledge OpenKIM's role in pioneering open, reproducible computational science and materials informatics. If a paper is missing from this list, let us know by contacting us at support@openkim.org.


2026

  • O. Loveday, K. Kaźmierczak, N. López, "Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis", ACS Catalysis, 16, 5, 4113-4124 (2026). doi:10.1021/acscatal.5c08945

  • M. Salas, A. Singh, C. Pignataro, L. Pal, "AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing", Communications Materials, 7, 1 (2026). doi:10.1038/s43246-026-01105-0


2025

  • И. Косарев, М. Хазимуллин, С. Дмитриев, "ОБЗОР ИССЛЕДОВАНИЙ ПО АНАЛИЗУ, ТЕСТИРОВАНИЮ И МАШИННОМУ ОБУЧЕНИЮ МЕЖАТОМНЫХ ПОТЕНЦИАЛОВ С ПРИМЕНЕНИЕМ ДЕЛОКАЛИЗОВАННЫХ НЕЛИНЕЙНЫХ КОЛЕБАТЕЛЬНЫХ МОД", Фундаментальные проблемы современного материаловедения, **, 4(22), 396-403 (2025). doi:10.25712/ASTU.1811-1416.2025.04.002

  • A. Karpatne, A. Deshwal, X. Jia, W. Ding, M. Steinbach, A. Zhang, V. Kumar, "AI-enabled scientific revolution in the age of generative AI: second NSF workshop report", npj Artificial Intelligence, 1, 1 (2025). doi:10.1038/s44387-025-00018-6

  • J. Xia, Y. Zhang, B. Jiang, "The evolution of machine learning potentials for molecules, reactions and materials", Chemical Society Reviews, 54, 10, 4790-4821 (2025). doi:10.1039/d5cs00104h

  • M. K. Horton, P. Huck, R. X. Yang, J. M. Munro, S. Dwaraknath, A. M. Ganose, R. S. Kingsbury, M. Wen, J. X. Shen, T. S. Mathis, A. D. Kaplan, K. Berket, J. Riebesell, J. George, A. S. Rosen, E. W. C. Spotte-Smith, M. J. McDermott, O. A. Cohen, A. Dunn, M. C. Kuner, G. Rignanese, G. Petretto, D. Waroquiers, S. M. Griffin, J. B. Neaton, D. C. Chrzan, M. Asta, G. Hautier, S. Cholia, G. Ceder, S. P. Ong, A. Jain, K. A. Persson, "Accelerated data-driven materials science with the Materials Project", Nature Materials, 24, 10, 1522-1532 (2025). doi:10.1038/s41563-025-02272-0

  • S. Talu, L. Vu Truong, "The power of simulation: Exploring binary alloys for next-generation applications", Journal of Nanomaterials and Applications, 1, 1, 1-16 (2025). doi:10.65273/hhit.jna.2025.1.1.1-16

  • Y. Zhang, V. Sorkin, Z. H. Aitken, A. Politano, J. Behler, A. P Thompson, T. W. Ko, S. P. Ong, O. Chalykh, D. Korogod, E. Podryabinkin, A. Shapeev, J. Li, Y. Mishin, Z. Pei, X. Liu, J. Kim, Y. Park, S. Hwang, S. Han, K. Sheriff, Y. Cao, R. Freitas, "Roadmap for the development of machine learning-based interatomic potentials", Modelling and Simulation in Materials Science and Engineering, 33, 2, 023301 (2025). doi:10.1088/1361-651X/ad9d63

  • S. Joshi, A. Bucsek, D. C. Pagan, S. Daly, S. Ravindran, J. Marian, M. A. Bessa, S. R. Kalidindi, N. C. Admal, C. Reina, S. Ghosh, J. A. Warren, J. Viñals, E. B. Tadmor, "Integrated experiment and simulation co-design: A key infrastructure for predictive mesoscale materials modeling", Mechanics of Materials, 211, 105480 (2025). doi:10.1016/j.mechmat.2025.105480

  • Y. Xian, C. Li, Y. Xu, Y. Zhou, D. Xue, "AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management", SusMat, 5, 5 (2025). doi:10.1002/sus2.70030

  • S. Xu, J. Wu, Y. Guo, Q. Zhang, X. Zhong, J. Li, W. Ren, "Applications of machine learning in surfaces and interfaces", Chemical Physics Reviews, 6, 1 (2025). doi:10.1063/5.0244175


2024

  • N. M. A. Krishnan, H. Kodamana, R. Bhattoo, Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications, Springer (2024). doi:10.1007/978-3-031-44622-1

  • Z. Li, Y. Zhang, T. Zhou, G. Jia, "Accelerating electrocatalyst design for CO2 conversion through machine learning: Interpretable models and data-driven innovations", Nexus, 1, 3, 100029 (2024). doi:10.1016/j.ynexs.2024.100029

  • Z. Wang, A. Chen, K. Tao, Y. Han, J. Li, "MatGPT: A Vane of Materials Informatics from Past, Present, to Future", Advanced Materials, 36, 6 (2024). doi:10.1002/adma.202306733

  • A. Lindsley, S. Ramachandran, C. Llebot, S. Fu, "Engineering Data Repositories and Open Science Compliance: A Guide for Engineering Faculty and Librarians", 2024 ASEE Annual Conference & Exposition Proceedings, ** (None). doi:10.18260/1-2–47274

  • D. Sivan, K. Satheesh Kumar, A. Abdullah, V. Raj, I. I. Misnon, S. Ramakrishna, R. Jose, "Advances in materials informatics: a review", Journal of Materials Science, 59, 7, 2602-2643 (2024). doi:10.1007/s10853-024-09379-w

  • J. Akhtar, T. Kalita, B. R. Thakuria, M. J. Sarmah, H. P. Goswami, "Deep Diving into AI-Enhanced Innovative Approaches in Designing Inorganic Nanomaterials", in Multifunctional Inorganic Nanomaterials for Energy Applications, edtied by H.P. Nagaswarupa, Mika E.T. Sillanpää, H.C. Ananda Murthy, Ramachandra Naik, pp. 382-394. Boca Raton:CRC Press, 2024. doi:10.1201/9781003479239-26

  • P. Brault, "Practical classical molecular dynamics simulations for low-temperature plasma processing: a review", Reviews of Modern Plasma Physics, 8, 1 (2024). doi:10.1007/s41614-023-00140-5

  • A. Walsh, "Open computational materials science", Nature Materials, 23, 1, 16-17 (2024). doi:10.1038/s41563-023-01699-7

  • L. C. Brinson, L. M. Bartolo, B. Blaiszik, D. Elbert, I. Foster, A. Strachan, P. W. Voorhees, "Community action on FAIR data will fuel a revolution in materials research", MRS Bulletin, 49, 1, 12-16 (2024). doi:10.1557/s43577-023-00498-4


2023

  • A. Seko, "Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems", Journal of Applied Physics, 133, 1 (2023). doi:10.1063/5.0129045

  • M. H. Müser, S. V. Sukhomlinov, L. Pastewka, "Interatomic potentials: achievements and challenges", Advances in Physics: X, 8, 1 (2023). doi:10.1080/23746149.2022.2093129

  • M. Enoki, S. Minamoto, I. Ohnuma, T. Abe, H. Ohtani, "Current Status and Future Scope of Phase Diagram Studies", ISIJ International, 63, 3, 407-418 (2023). doi:10.2355/isijinternational.ISIJINT-2022-408

  • G. Kanagalingam, S. Schmitt, F. Fleckenstein, S. Stephan, "Data scheme and data format for transferable force fields for molecular simulation", Scientific Data, 10, 1 (2023). doi:10.1038/s41597-023-02369-8

  • K. Höflich, G. Hobler, F. I. Allen, T. Wirtz, G. Rius, L. McElwee-White, A. V. Krasheninnikov, M. Schmidt, I. Utke, N. Klingner, M. Osenberg, R. Córdoba, F. Djurabekova, I. Manke, P. Moll, M. Manoccio, J. M. De Teresa, L. Bischoff, J. Michler, O. De Castro, A. Delobbe, P. Dunne, O. V. Dobrovolskiy, N. Frese, A. Gölzhäuser, P. Mazarov, D. Koelle, W. Möller, F. Pérez-Murano, P. Philipp, F. Vollnhals, G. Hlawacek, "Roadmap for focused ion beam technologies", Applied Physics Reviews, 10, 4 (2023). doi:10.1063/5.0162597

  • C. 城. Zhang 张, X. 小. Fu 付, "Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives", Chinese Physics B, 32, 12, 126103 (2023). doi:10.1088/1674-1056/ad01a4

  • G. Anand, S. Ghosh, L. Zhang, A. Anupam, C. L. Freeman, C. Ortner, M. Eisenbach, J. R. Kermode, "Exploiting Machine Learning in Multiscale Modelling of Materials", Journal of The Institution of Engineers (India): Series D, 104, 2, 867-877 (2023). doi:10.1007/s40033-022-00424-z

  • K. N. Quinn, M. C. Abbott, M. K. Transtrum, B. B. Machta, J. P. Sethna, "Information geometry for multiparameter models: new perspectives on the origin of simplicity", Reports on Progress in Physics, 86, 3, 035901 (2023). doi:10.1088/1361-6633/aca6f8

  • A. Hagg, K. N. Kirschner, "Open-Source Machine Learning in Computational Chemistry", Journal of Chemical Information and Modeling, 63, 15, 4505-4532 (2023). doi:10.1021/acs.jcim.3c00643

  • T. W. Ko, S. P. Ong, "Recent advances and outstanding challenges for machine learning interatomic potentials", Nature Computational Science, 3, 12, 998-1000 (2023). doi:10.1038/s43588-023-00561-9

  • D. Duffy, "TYC Materials Modelling Course: Interatomic Potentials", Thomas Young Centre (2023). link

  • L. M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. T. Koch, M. Kühbach, A. N. Ladines, P. Lambrix, M. Himmer, S. V. Levchenko, M. Oliveira, A. Michalchuk, R. E. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, M. Scheffler, "Shared metadata for data-centric materials science", Scientific Data, 10, 1 (2023). doi:10.1038/s41597-023-02501-8


2022

  • Z. Wang, Z. Sun, H. Yin, X. Liu, J. Wang, H. Zhao, C. H. Pang, T. Wu, S. Li, Z. Yin, X. Yu, "Data‐Driven Materials Innovation and Applications", Advanced Materials, 34, 36 (2022). doi:10.1002/adma.202104113

  • L. Boeri, R. Hennig, P. Hirschfeld, G. Profeta, A. Sanna, E. Zurek, W. E. Pickett, M. Amsler, R. Dias, M. I. Eremets, C. Heil, R. J. Hemley, H. Liu, Y. Ma, C. Pierleoni, A. N. Kolmogorov, N. Rybin, D. Novoselov, V. Anisimov, A. R. Oganov, C. J. Pickard, T. Bi, R. Arita, I. Errea, C. Pellegrini, R. Requist, E. K. U. Gross, E. R. Margine, S. R. Xie, Y. Quan, A. Hire, L. Fanfarillo, G. R. Stewart, J. J. Hamlin, V. Stanev, R. S. Gonnelli, E. Piatti, D. Romanin, D. Daghero, R. Valenti, "The 2021 room-temperature superconductivity roadmap", Journal of Physics: Condensed Matter, 34, 18, 183002 (2022). doi:10.1088/1361-648X/ac2864

  • T. Wen, L. Zhang, H. Wang, W. E, D. J. Srolovitz, "Deep potentials for materials science", Materials Futures, 1, 2, 022601 (2022). doi:10.1088/2752-5724/ac681d

  • R. X. Yang, C. A. McCandler, O. Andriuc, M. Siron, R. Woods-Robinson, M. K. Horton, K. A. Persson, "Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis", ACS Nano, 16, 19873-19891 (2022).

  • E. M. Campo, S. Shankar, A. S. Szalay, R. J. Hanisch, "Now Is the Time to Build a National Data Ecosystem for Materials Science and Chemistry Research Data", ACS Omega, 7, 16, 13398-13402 (2022). doi:10.1021/acsomega.2c00905


2021

  • T. Wang, "Brief Introduction of the Machine Learning Method", in: Y.Cheng, T. Wang, G. Zhang (eds), Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312, pp. 1-20. Springer, Cham (2021). doi:10.1007/978-3-030-68310-8_1

  • T. Bereau, "Computational compound screening of biomolecules and soft materials by molecular simulations", Modelling and Simulation in Materials Science and Engineering, 29, 2, 023001 (2021). doi:10.1088/1361-651X/abd042

  • O. Heinz, H. Heinz, "Cement Interfaces: Current Understanding, Challenges, and Opportunities", Langmuir, 37, 21, 6347-6356 (2021). doi:10.1021/acs.langmuir.1c00617


2020

  • G. Pilania, P. V. Balachandran, J. E. Gubernatis, T. Lookman, "Learning with Large Databases", in Data-Based Methods for Materials Design and Discovery. Synthesis Lectures on Materials and Optics, pp. 59-86, Springer, Cham (2020). doi:10.1007/978-3-031-02383-5_3

  • S.-W. Kim and S.-T. Kim, "A Study on Research Data Management Strategies in Condensed Matter Physics" Journal of the Korean Society for Information Management, 37, 77-106 (2020). doi:10.3743/KOSIM.2020.37.3.077

  • D. Matsunaka, "Introduction to molecular dynamics and its application to mechanical behaviors", Journal of Japan Institute of Light Metals, 70, 3, 107-112 (2020). doi:10.2464/jilm.70.107

  • W. Smith, M. Guest, I. Todorov, P. Durham, "Molecular simulation and the collaborative computational projects", The European Physical Journal H, 45, 4-5, 259-343 (2020). doi:10.1140/epjh/e2020-10034-9

  • A. Rizzo, S. Goel, M. Luisa Grilli, R. Iglesias, L. Jaworska, V. Lapkovskis, P. Novak, B. O. Postolnyi, D. Valerini, "The Critical Raw Materials in Cutting Tools for Machining Applications: A Review", Materials, 13, 6, 1377 (2020). doi:10.3390/ma13061377

  • T. Wang, C. Zhang, H. Snoussi, G. Zhang, "Machine Learning Approaches for Thermoelectric Materials Research", Advanced Functional Materials, 30, 5 (2020). doi:10.1002/adfm.201906041

  • M. W. Thompson, J. B. Gilmer, R. A. Matsumoto, C. D. Quach, P. Shamaprasad, A. H. Yang, C. R. Iacovella, C. McCabe, P. T. Cummings, "Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)", Molecular Physics, 118, 9-10, e1742938 (2020). doi:10.1080/00268976.2020.1742938

  • G. J. A. Sevink, J. A. Liwo, P. Asinari, D. MacKernan, G. Milano, I. Pagonabarraga, "Unfolding the prospects of computational (bio)materials modeling", The Journal of Chemical Physics, 153, 10 (2020). doi:10.1063/5.0019773


2019

  • D. L. McDowell, "Multiscale Modeling of Interfaces, Dislocations, and Dislocation Field Plasticity", in S. Mesarovic, S. Forest, H, Zbib (eds), Mesoscale Models. CISM International Centre for Mechanical Sciences, vol 587, pp. 195-297. Springer, Cham (2019). doi:10.1007/978-3-319-94186-8_5

  • N. Artrith, "Machine learning for the modeling of interfaces in energy storage and conversion materials", Journal of Physics: Energy, 1, 3, 032002 (2019). doi:10.1088/2515-7655/ab2060

  • L. Himanen, A. Geurts, A. S. Foster, P. Rinke, "Data‐Driven Materials Science: Status, Challenges, and Perspectives", Advanced Science, 6, 21 (2019). doi:10.1002/advs.201900808

  • National Academies of Sciences, Engineering, and Medicine. 2019. Frontiers of Materials Research: A Decadal Survey. Washington, DC: The National Academies Press. doi:10.17226/25244

  • S. Ramakrishna, T. Zhang, W. Lu, Q. Qian, J. S. C. Low, J. H. R. Yune, D. Z. L. Tan, S. Bressan, S. Sanvito, S. R. Kalidindi, "Materials informatics", Journal of Intelligent Manufacturing, 30, 6, 2307-2326 (2019). doi:10.1007/s10845-018-1392-0


2018

  • A. E. Romanov, M. A. Rozhkov, A. L. Kolesnikova, "Disclinations in polycrystalline graphene and pseudo-graphenes. Review", Letters on Materials, 8, 4, 384-400 (2018). doi:10.22226/2410-3535-2018-4-384-400

  • "La prochaine révolution de la production: Conséquences pour les pouvoirs publics et les entreprises", Éditions OCDE, Paris (2018). doi:10.1787/9789264280793-10-fr

  • D. L. McDowell, "Multiscale Crystalline Plasticity for Materials Design", in D. Shin, J. Saal (eds), Computational Materials System Design, pp. 105-146. Springer, Cham (2018). doi:10.1007/978-3-319-68280-8_6

  • J. A. Warren, C. H. Ward, "Evolution of a Materials Data Infrastructure", JOM, 70, 9, 1652-1658 (2018). doi:10.1007/s11837-018-2968-z

  • R. Jose, S. Ramakrishna, "Materials 4.0: Materials big data enabled materials discovery", Applied Materials Today, 10, 127-132 (2018). doi:10.1016/j.apmt.2017.12.015


2017

  • H. Chung, B. J. Braams, "Developments in Data Exchange (Summary Report of an IAEA Consultants Meeting)", IAEA Nuclear Data Section (2017). doi:10.61092/iaea.9p7w-5msj

  • L. M. Ghiringhelli, C. Carbogno, S. Levchenko, F. Mohamed, G. Huhs, M. Lüders, M. Oliveira, M. Scheffler, "Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats", npj Computational Materials, 3, 1 (2017). doi:10.1038/s41524-017-0048-5

  • W. Gerberich, E. B. Tadmor, J. Kysar, J. A. Zimmerman, A. M. Minor, I. Szlufarska, J. Amodeo, B. Devincre, E. Hintsala, R. Ballarini, "Review Article: Case studies in future trends of computational and experimental nanomechanics", Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 35, 6 (2017). doi:10.1116/1.5003378


2016

  • S. R. Kalidindi, D. B. Brough, S. Li, A. Cecen, A. L. Blekh, F. Y. P. Congo, C. Campbell, "Role of materials data science and informatics in accelerated materials innovation", MRS Bulletin, 41, 08, 596-602 (2016). doi:10.1557/mrs.2016.164

  • D. L. McDowell, S. R. Kalidindi, "The materials innovation ecosystem: A key enabler for the Materials Genome Initiative", MRS Bulletin, 41, 4, 326-337 (2016). doi:10.1557/mrs.2016.61

  • J. Hill, G. Mulholland, K. Persson, R. Seshadri, C. Wolverton, B. Meredig, "Materials science with large-scale data and informatics: Unlocking new opportunities", MRS Bulletin, 41, 5, 399-409 (2016). doi:10.1557/mrs.2016.93

  • A. Jain, K. A. Persson, G. Ceder, "Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases", APL Materials, 4, 5 (2016). doi:10.1063/1.4944683


2015

  • P. Brommer, A. Kiselev, D. Schopf, P. Beck, J. Roth, H. Trebin, "Classical interaction potentials for diverse materials fromab initiodata: a review ofpotfit", Modelling and Simulation in Materials Science and Engineering, 23, 7, 074002 (2015). doi:10.1088/0965-0393/23/7/074002

  • "Applying Materials State Awareness to Condition-Based Maintenance and System Life Cycle Management", National Academies Press (2015). doi:10.17226/21821


2014

  • S. B. Sinnott, S. R. Phillpot, "Role of cyberinfrastructure in educating the next generation of computational materials scientists", Integrating Materials and Manufacturing Innovation, 3, 1, 85-89 (2014). doi:10.1186/2193-9772-3-7


2013

  • Y. Dong, Q. Li, A. Martini, "Molecular dynamics simulation of atomic friction: A review and guide", Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 31, 3 (2013). doi:10.1116/1.4794357

  • S. B. Sinnott, "Material design and discovery with computational materials science", Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 31, 5 (2013). doi:10.1116/1.4813689


2012

  • S. J. Plimpton, A. P. Thompson, "Computational aspects of many-body potentials", MRS Bulletin, 37, 5, 513-521 (2012). doi:10.1557/mrs.2012.96


2011

  • P. A. Schultz, "Nuclear Energy Advanced Modeling and Simulation Waste Integrated Performance and Safety Codes (NEAMS Waste IPSC): Subcontinuum-scale Verification and Validation Strategy", Office of Scientific and Technical Information (OSTI), Sandia Report SAND2011-9257 (2011). doi:10.2172/1034882


Scientific papers

This is a partial list of papers in which OpenKIM functionality was used in scientific applications. If a paper is missing from this list, let us know by contacting us at support@openkim.org. (Note that this list only includes articles by researchers unaffiliated with OpenKIM. For articles authored by KIM team members, see Publications by the KIM project)


2026

  • E. Holbrook, J. C. Verduzco, A. Strachan, "Evaluating LLM-generated code for domain-specific languages: molecular dynamics with LAMMPS", arXiv (2026). doi:10.48550/arXiv.2603.20630

  • X. Liu, G. Chen, Y. Zhang, J. Qi, P. Mao, "Investigation of strengthening mechanisms in gradient nanostructured polycrystalline magnesium using machine learning and molecular dynamics", Materials Letters, 403, 139547 (2026). doi:10.1016/j.matlet.2025.139547

  • J. G. Threadingham, X. Liang, E. Leggett, L. C. Smith, J. C. F. Millett, G. Whiteman, V. Peçanha-Antonio, A. T. Boothroyd, D. J. Chapman, D. E. Eakins, "Effects of crystal orientation on the shock properties of single crystal tin", Journal of Applied Physics, 139, 6 (2026). doi:10.1063/5.0309788

  • A. Das, R. Gosh, "Effect of interionic interaction on atomic diffusion of liquid 3d transition metals: A theoretical and simulation based study", Physica B: Condensed Matter, 720, 417982 (2026). doi:10.1016/j.physb.2025.417982

  • M. Antoniou, D. Varsou, A. Tsoumanis, G. Melagraki, I. Lynch, A. Afantitis, "Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicity via the Enalos Cloud platform", Nanoscale Advances, 8, 2, 646-661 (2026). doi:10.1039/d5na00549c

  • D. Varsou, A. Theodori, A. G. Papadiamantis, A. Tsoumanis, D. Zouraris, M. Antoniou, N. Koutroumpa, G. Melagraki, I. Lynch, A. Afantitis, "Rigorous data curation, enrichment and meta-analysis enable autoML prediction of plant length responses to nanoparticles powered by the Enalos Cloud platform", Environmental Science: Nano, 13, 1, 621-640 (2026). doi:10.1039/d5en00897b

  • M. U. Farooq, N. Ahmed, M. Y. Muneeb, F. Chen, "Intelligent navigation of potential energy surfaces: leveraging deep reinforcement learning paradigms for accelerated discovery of stable nickel nanoclusters", Nanoscale, 18, 6, 3245-3261 (2026). doi:10.1039/d5nr04468e

  • D. Varsou, A. Theodori, A. G. Papadiamantis, A. Tsoumanis, D. Zouraris, M. Antoniou, N. Koutroumpa, G. Melagraki, I. Lynch, A. Afantitis, "Rigorous data curation, enrichment and meta-analysis enable autoML prediction of plant length responses to nanoparticles powered by the Enalos Cloud platform", Environmental Science: Nano, 13, 1, 621-640 (2026). doi:10.1039/D5EN00897B

  • H. Li, Q. Chen, T. Gao, Q. Li, Z. Zhang, K. Dong, X. Luo, S. Chen, "Dislocation evolution and cyclic hardness enhancement of GaAs induced by cyclic nanoindentation with different shape indenters", Journal of Science: Advanced Materials and Devices, 11, 2, 101129 (2026). doi:10.1016/j.jsamd.2026.101129

  • W. Ding, H. Zhang, E. Huo, "Mechanical Properties of FeCoCrNi-based Quinary HEAs by Molecular Dynamics Simulation", Springer Proceedings in Physics, **, 693-706 (2026). doi:10.1007/978-981-95-3449-4_54

  • T. Huang, M. Yang, Q. Li, Y. Su, X. Guo, X. Dai, S. Zhang, T. Goto, R. Tu, L. Zhang, "Atomistic description of friction and phase transformation behaviors in nanoscratch of AISI 304/DLC system", Tribology International, 219, 111866 (2026). doi:10.1016/j.triboint.2026.111866

  • S. Ogane, K. Moriguchi, "Molecular dynamics study of stress-induced phase transition and long-period polytype formation via bain-path deformation", MRS Advances, ** (2026). doi:10.1557/s43580-026-01540-8

  • B. Wu, A. Martínez, P. Obladen, M. Fernández-Lomana, E. Herrera, C. Sabater, J. J. Palacios, I. Guillamón, H. Suderow, "Conductance of atomic size contacts of Ag and Au at high magnetic fields", Physical Review Research, 8, 1 (2026). doi:10.1103/cmh2-frmf

  • M. Valderrama, N. Fillot, D. Dini, J. P. Ewen, "FrictionSim2D: A High-Throughput Molecular Simulation Framework to Screen the Friction of Two-Dimensional Materials", American Chemical Society (ACS) (2026). doi:10.26434/chemrxiv.15001138/v1

  • G. LeCroy, R. Austin, R. Gakhar, A. Williams, "Quantitative Analysis of Fission-Product Surrogates in Molten Salt Chloride Aerosols", Photonics, 13, 1, 93 (2026). doi:10.3390/photonics13010093

  • P. Udommai, K. Panthinuan, N. Tanasanchai, C. Pilapong, S. Sakulsermsuk, W. Anukool, "Toward a new method to measure the temperature-dependent absorptivity of solid and molten rubidium", Physica B: Condensed Matter, 724, 418179 (2026). doi:10.1016/j.physb.2025.418179

  • N. L. D. Sui, Y. Li, Y. Sun, T. Yoshida, H. Ariga‐Miwa, T. Uruga, Z. Zhang, X. Long, G. Wang, Y. Iwasawa, J. Lee, "Synergistic High Entropy and Inductive Effects in Aerogels Accelerate pH‐Universal Hydrogen Evolution", Small, 22, 10 (2026). doi:10.1002/smll.202512101

  • M. Antoniou, D. Varsou, A. Tsoumanis, G. Melagraki, I. Lynch, A. Afantitis, "Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicity via the Enalos Cloud platform", Nanoscale Advances, 8, 2, 646-661 (2026). doi:10.1039/D5NA00549C

  • A. Tai, J. Ogbebor, R. Freitas, "Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering", arXiv (2026). doi:10.48550/arXiv.2601.05003

  • S. A. Policastro, R. M. Anderson, E. A. Shockley, J. A. Keith, "Work Function Signatures of Oxide and Sulfide Formation on Cu–Ni Alloy Surfaces", The Journal of Physical Chemistry C, 130, 8, 3191-3200 (2026). doi:10.1021/acs.jpcc.5c08561

  • P. D. Kolokathis, A. Sourpis, D. Mintis, A. Tsoumanis, G. Melagraki, M. Velimirovic, I. Lynch, A. Afantitis, "HydroNanoConstruct: A Web Application for Digital Construction, Crystal Growth Investigation, and Atomistic Descriptor Calculation of Hydrated Metal Oxide Nanoparticles Powered by the EosCloud Platform", Journal of Chemical Information and Modeling, 66, 1, 1-6 (2026). doi:10.1021/acs.jcim.5c01889


2025

  • N. Chakraborti, "The advent of the deep learning evolutionary algorithm EvoDN2 and its recent applications", Computer Methods in Materials Science, 25, 2, 41-55 (2025). doi:10.7494/cmms.2025.2.1021

  • B. A. Panchenko, E. V. Fomin, A. E. Mayer, "Tensor equation of state for copper and aluminum", Computational Materials Science, 253, 113845 (2025). doi:10.1016/j.commatsci.2025.113845

  • T. Huang, M. Yang, Y. Su, Y. Han, Q. Li, S. Zhang, T. Goto, R. Tu, L. Zhang, "Molecular dynamics study on nano sliding behavior at DLC/AISI 304 interface", Carbon, 240, 120371 (2025). doi:10.1016/j.carbon.2025.120371

  • M. S. Hasan, H. Bayat, W. de Jong, W. Xu, "Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites", Materials & Design, 260, 115063 (2025). doi:10.1016/j.matdes.2025.115063

  • Y. Zheng, Y. Si, L. Hu, S. Liu, W. Zhai, B. Wei, "Peritectic solidification mechanism of substantially undercooled liquid Fe-Y-B ternary alloys", Journal of Alloys and Compounds, 1010, 177526 (2025). doi:10.1016/j.jallcom.2024.177526

  • L. Lu, W. Cao, R. Botella, "Screener and enumerator with force-field optimization (SEFFO): Algorithm for searching adsorption sites and configurations on 2D materials", Computer Physics Communications, 308, 109440 (2025). doi:10.1016/j.cpc.2024.109440

  • Y. Yan, G. Lu, Y. Lei, "Effects of the Grain Size and Temperature on the Tensile Behavior of Nanopolycrystalline Niobium", Journal of Applied Mechanics and Technical Physics, 66, 1, 169-177 (2025). doi:10.1134/S002189442501016X

  • L. Dammann, R. Kohns, P. Huber, R. H. Meißner, "Maximum Entropy-Mediated Liquid-to-Solid Nucleation and Transition", Journal of Chemical Theory and Computation, 21, 4, 1997-2011 (2025). doi:10.1021/acs.jctc.4c01621

  • S. R. Maalouf, S. S. Vel, "Nonlinear elastic response of 2D materials under simultaneous in-plane strains and flexural deformations", International Journal of Engineering Science, 214, 104270 (2025). doi:10.1016/j.ijengsci.2025.104270

  • S. V. Belim, I. V. Tikhomirov, "Monte Carlo Modeling of the Graphene Moiré Structure on an Ir(111) Substrate", Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques, 19, 3, 710-716 (2025). doi:10.1134/S1027451025701058

  • M. A. Mubeen, F. Chen, "Deep reinforcement learning unveils ternary nanocluster configurations: A case study on Ag6Pd5Cu4", Journal of Applied Physics, 137, 22 (2025). doi:10.1063/5.0268140

  • S. Yuan, K. Kim, B. Wang, W. Jeong, T. W. Heo, B. C. Wood, L. F. Wan, "Strain perturbation method for atomic stress calculation with machine-learning potentials", Physical Review Research, 7, 3 (2025). doi:10.1103/jg6w-fdl8

  • S. Liu, S. Chen, W. Zhang, X. Ye, Y. Zhang, Z. Zhang, "New perspective into the atomic-scale local plasticity behavior of Ni-based single crystal superalloys", Materials Science and Engineering: A, 946, 149032 (2025). doi:10.1016/j.msea.2025.149032

  • S. Liu, X. Ye, Y. Zhang, X. Zhao, Z. Zhang, "Dislocation-mediated rejuvenation of deformation energy in a novel nickel-based single crystal superalloy", Materials Characterization, 229, 115523 (2025). doi:10.1016/j.matchar.2025.115523

  • F. XU, Y. ZHENG, S. LIU, W. ZHAI, B. WEI, , , "Thermophysical properties and rapid solidification mechanism of highly undercooled liquid Fe-Nd-Y-B alloy under electrostatic levitation condition", Acta Physica Sinica, 74, 19, 197102 (2025). doi:10.7498/aps.74.20250904

  • A. Hassanzadeh, G. Alahyarizadeh, H. Kargaran, "UO2-GaN composites with enhanced thermomechanical and thermophysical properties: A comprehensive molecular dynamics study", Next Materials, 9, 101358 (2025). doi:10.1016/j.nxmate.2025.101358

  • E. Spirande, T. Miryashkin, A. Kolmakov, A. Shapeev, "Automated prediction of thermodynamic properties via Bayesian free-energy reconstruction from molecular dynamics", Computational Condensed Matter, 45, e01163 (2025). doi:10.1016/j.cocom.2025.e01163

  • M. A. Mubeen, F. Chen, "Deep reinforcement learning for AgPd-based multimetallic nanoclusters: Accelerating global minimum discovery in high-entropy alloy systems", The Journal of Chemical Physics, 163, 17 (2025). doi:10.1063/5.0291364

  • T. Barnowsky, C. Timm and R. Friedrich, "Exfoliation and Cleavage of Crystals from a Universal Potential", arXiv (2025). doi:10.48550/arXiv.2512.16721

  • E. L. Hung, Z. Xu, G. S. Rohrer, "Planar coincident site density is not a reliable predictor of grain boundary energy", Materialia, 42, 102453 (2025). doi:10.1016/j.mtla.2025.102453

  • Z. Wei, Y. Xie, W. Zhang, N. Qu, Y. Liu, Y. Xiao, L. Sun, B. Wu, J. Zhu, "Effects of Temperature and Strain Rate on the Mechanical Behavior of Cu-Cr-Zr Alloys: A Molecular Dynamics Simulation", Elsevier BV (2025). doi:10.2139/ssrn.5128876

  • Z. Qin, L. Lin, M. Bai, J. Liu, C. Li, W. Liu, L. Wang, "Investigation of Interfacial Thermal Resistance in Diamond/CNT Heterostructures via Non-Equilibrium Molecular Dynamics", Elsevier BV (2025). doi:10.2139/ssrn.5928821

  • E. Chen, T. Frolov, "Quasiaperiodic grain boundary phases of Σ5 tilt grain boundaries in refractory metals", Physical Review B, 112, 6 (2025). doi:10.1103/vsgc-gkdx

  • X. Li, H. Wu, W. Gao, Q. Jiang, "A roadmap from the bond strength to the grain-boundary energies and macro strength of metals", Nature Communications, 16, 1 (2025). doi:10.1038/s41467-025-55921-y

  • R. Batabyal, M. Manna, S. Mukherjee, S. Giri, P. Bhakuni, S. Barman, A. Pariari, A. Gome, M. Hücker, V. R. Reddy, A. Roy, S. R. Barman, S. Karmakar, C. Mondal, "Escape-Induced Temporally Correlated Noise Driven Crossover in Growth Kinetics and Universality Class", Springer Science and Business Media LLC (2025). doi:10.21203/rs.3.rs-7934313/v1

  • A. Gerami, M. Silani, M. Javanbakht, "Comparative analysis of local and nonlocal elasticity theories at the nanoscale via FEM and MD simulations", Mechanics of Advanced Materials and Structures, **, 1-18 (2025). doi:10.1080/15376494.2025.2591165

  • T. Göynük, Z. Esen, İ. Karakaya, "Interfacial behavior and diffusion mechanisms of BNi-2 brazing on titanium alloy: experimental and molecular dynamics insights", Journal of Molecular Modeling, 31, 7 (2025). doi:10.1007/s00894-025-06429-1

  • P. Saxe, J. Nash, M. Mostafanejad, E. Marin-Rimoldi, H. Hafiz, L. G. Hector, T. D. Crawford, "SEAMM: A Simulation Environment for Atomistic and Molecular Modeling", The Journal of Physical Chemistry A, 129, 30, 6973-6993 (2025). doi:10.1021/acs.jpca.5c03164

  • A. Mirza, N. Alampara, M. Ríos-García, M. Abdelalim, J. Butler, B. Connolly, T. Dogan, M. Nezhurina, B. Şen, S. Tirunagari, M. Worrall, A. Young, P. Schwaller, M. Pieler, K. M. Jablonka, "ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models", arXiv (2025). doi:10.48550/arXiv.2505.12534

  • K. Choudhary, "The JARVIS infrastructure is all you need for materials design", Computational Materials Science, 259, 114063 (2025). doi:10.1016/j.commatsci.2025.114063

  • A. M. Barboza, L. C. R. Aliaga, D. F. de Faria, I. N. Bastos, "Unveiling the structure and interactions of silicene grown on MoS 2 : insights from hybrid molecular dynamics simulations", Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials, 81, 6, 533-539 (2025). doi:10.1107/S2052520625009187

  • M. Thoms, H. Sun, L. K. Béland, "Benchmarking 34 OpenKIM Nickel Potentials with an Emphasis on Surfaces and Extended Defects", arXiv (2025). doi:10.48550/arXiv.2510.18033

  • F. Shuang, K. Liu, Y. Ji, W. Gao, L. Laurenti, P. Dey, "Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction", npj Computational Materials, 11, 1 (2025). doi:10.1038/s41524-025-01599-1

  • Y. Choi, T. Brink, "Faceting transition in aluminum as a grain boundary phase transition", Physical Review Materials, 9, 8 (2025). doi:10.1103/2dnf-zdz8

  • M. Ataei, M. Modarresi, M. R. Roknabadi, A. Mogulkoc, "Molecular dynamics study of corrugation in low-defect graphene using machine learning potential", Physica Scripta, 100, 8, 086012 (2025). doi:10.1088/1402-4896/adf897

  • S. Burlison, M. F. Becker, D. Kovar, "A molecular dynamics study of high velocity impact of zinc oxide aggregates", Journal of Aerosol Science, 187, 106582 (2025). doi:10.1016/j.jaerosci.2025.106582

  • Y. Couzinié, Y. Seki, Y. Nishiya, H. Nishi, T. Kosugi, S. Tanaka, Y. Matsushita, "Machine Learning Supported Annealing for Prediction of Grand Canonical Crystal Structures", Journal of the Physical Society of Japan, 94, 4 (2025). doi:10.7566/JPSJ.94.044802


2024

  • J. J. Winetrout, K. Kanhaiya, J. Kemppainen, P. J. in ‘t Veld, G. Sachdeva, R. Pandey, B. Damirchi, A. van Duin, G. M. Odegard, H. Heinz, "Implementing reactivity in molecular dynamics simulations with harmonic force fields", Nature Communications, 15, 7945 (2024). doi:10.1038/s41467-024-50793-0

  • C. Ding, Y. Lei, X. Wang, X. Li, X. Li, Y. Zhang, Y. Xu, C. Liu, X. Wu, "A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten", Tungsten, 6, 2, 304-322 (2024). doi:10.1007/s42864-023-00230-4

  • Q. Zhang, T. Liu, H. Guo, Y. Chen, Y. Di, Z. Zhang, F. Wang, "Lead‐Induced Microstrain in Synthesis and Manipulation of Porous Pyrochlore for Boosting Oxygen Evolution Reaction", Advanced Functional Materials, 34, 3 (2024). doi:10.1002/adfm.202306176

  • R. Isozaki, Y. Shibuta, "Molecular Dynamic Simulation of Kinetics of fcc–bcc Heterointerface in Phase Transformation of Iron and Carbon Steel", ISIJ International, 64, 2, 184-191 (2024). doi:10.2355/isijinternational.ISIJINT-2023-153

  • A. Hegde, E. Weiss, W. Windl, H. N. Najm, C. Safta, "A Bayesian calibration framework with embedded model error for model diagnostics", International Journal for Uncertainty Quantification, 14, 6, 37-70 (2024). doi:10.1615/Int.J.UncertaintyQuantification.2024051602

  • Y. Wang, S. Patel, C. Ortner, "A theoretical case study of the generalization of machine-learned potentials", Computer Methods in Applied Mechanics and Engineering, 422, 116831 (2024). doi:10.1016/j.cma.2024.116831

  • C. Nuñez Valencia, W. B. Lomholdt, M. H. Leth Larsen, T. W. Hansen, J. Schiøtz, "Beam induced heating in electron microscopy modeled with machine learning interatomic potentials", Nanoscale, 16, 11, 5750-5759 (2024). doi:10.1039/d3nr05220f

  • X. Liao, Y. Chen, R. Qiu, Y. Liu, N. Gao, F. Gao, W. Hu, H. Deng, "A new interatomic potential of W-Ni-Fe systems for point defects and mechanical property studies", Journal of Nuclear Materials, 594, 155040 (2024). doi:10.1016/j.jnucmat.2024.155040

  • N. S. Villa, L. Bonoldi, G. Assanelli, M. Notari, A. Lucotti, M. Tommasini, H. M. Cuppen, D. R. Galimberti, "Digging into the friction reduction mechanism of organic friction modifiers on steel surfaces: Chains packing vs. molecule–metal interactions", Tribology International, 195, 109649 (2024). doi:10.1016/j.triboint.2024.109649

  • D. Varsou, P. D. Kolokathis, M. Antoniou, N. K. Sidiropoulos, A. Tsoumanis, A. G. Papadiamantis, G. Melagraki, I. Lynch, A. Afantitis, "In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation", Computational and Structural Biotechnology Journal, 25, 47-60 (2024). doi:10.1016/j.csbj.2024.03.020

  • Y.-P. Yan, L.-T. Zhang, L.-P. Zhang, G. Lu, Z.-X. Tu, "Influence of temperature, stress, and grain size on behavior of nano-polycrystalline niobium", Chinese Physics B, 33, 7, 076201 (2024). doi:10.1088/1674-1056/ad3b83

  • Y. Zhang, F. Chen, Z. Liu, Y. Ju, D. Cui, J. Zhu, X. Jiang, X. Guo, J. He, L. Zhang, X. Zhang, Y. Su, "A materials terminology knowledge graph automatically constructed from text corpus", Scientific Data, 11, 1 (2024). doi:10.1038/s41597-024-03448-0

  • C. Hong, Y. Lu, H. Zheng, Z. Li, G. Guo, "Uniaxial tensile behaviors and Hall-Petch relationship of polycrystalline 316LN stainless steel via molecular dynamics simulation", Computational Materials Science, 244, 113195 (2024). doi:10.1016/j.commatsci.2024.113195

  • S. S. Vel, S. R. Maalouf, "Bending and twisting rigidities of 2D materials", International Journal of Mechanical Sciences, 280, 109501 (2024). doi:10.1016/j.ijmecsci.2024.109501

  • M. Wang, W. Guo, S. Lü, J. Wang, S. Wu, "Various rejuvenation behavior of Cu–Zr–Al-(Sn) metallic glass via deep cryogenic cycling treatment", Materials Science and Engineering: A, 912, 146994 (2024). doi:10.1016/j.msea.2024.146994

  • N. Tuchinda, C. A. Schuh, "Triple junction excess energy in polycrystalline metals", Acta Materialia, 279, 120274 (2024). doi:10.1016/j.actamat.2024.120274

  • S. Kounouho, R. Dingreville, J. Guilleminot, "Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles", Computer Methods in Applied Mechanics and Engineering, 431, 117323 (2024). doi:10.1016/j.cma.2024.117323

  • R. Wang, L. Zhu, S. Pattamatta, D. J. Srolovitz, Z. Wu, "The taming of the screw: Dislocation cores in BCC metals and alloys", Materials Today, 79, 36-48 (2024). doi:10.1016/j.mattod.2024.07.009

  • Y. Zhang, K. Jiao, J. Zhang, X. Fan, S. Gao, "Viscosity and structure correlation mechanism of Fe-Cr-C melt based on MD simulation", Journal of Molecular Liquids, 413, 125903 (2024). doi:10.1016/j.molliq.2024.125903

  • R. K. Raju, "Exploring Nanocluster Potential Energy Surfaces via Deep Reinforcement Learning: Strategies for Global Minimum Search", The Journal of Physical Chemistry A, 128, 42, 9122-9134 (2024). doi:10.1021/acs.jpca.4c04416

  • A. Farahvash, A. P. Willard, "A theory of phonon-induced friction on molecular adsorbates", Proceedings of the National Academy of Sciences, 121, 31 (2024). doi:10.1073/pnas.2400589121

  • N. Liang, X. Fu, J. Zhang, Z. Ruan, B. Qin, T. Ma, B. Long, "Evaluation of Wetting Behaviors of Liquid Sodium on Transition Metals: An Experimental and Molecular Dynamics Simulation Study", Materials, 17, 3, 691 (2024). doi:10.3390/ma17030691

  • S. V. Belim, I. V. Tikhomirov, "Moire Structures in Graphene on Cu (111) Substrate: Computer Simulation", Iranian Journal of Science, 48, 5, 1365-1372 (2024). doi:10.1007/s40995-024-01674-0

  • P. Ying, A. Natan, O. Hod, M. Urbakh, "Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study", ACS Nano, 18, 14, 10133-10141 (2024). doi:10.1021/acsnano.3c13099

  • N. A. Akil, "Length dependent thermal conductivity of silicon and copper nanowire: a molecular dynamics study", Molecular Crystals and Liquid Crystals, 768, 2, 132-142 (2024). doi:10.1080/15421406.2023.2243691

  • M. Lupo Pasini, M. Karabin, M. Eisenbach, "Transferable prediction of formation energy across lattices of increasing size", American Chemical Society (ACS) (2024). doi:10.26434/chemrxiv-2023-c14r3-v3

  • J. P. Mailoa, X. Li, S. Zhang, "3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization", Nature Communications, 15, 1 (2024). doi:10.1038/s41467-024-54453-1

  • X. Gong, Z. Li, A. S. L. S. Pattamatta, T. Wen, D. J. Srolovitz, "An accurate and transferable machine learning interatomic potential for nickel", Communications Materials, 5, 1 (2024). doi:10.1038/s43246-024-00603-3

  • P. Cuillier, M. G. Tucker, Y. Zhang, "Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile", Journal of Applied Crystallography, 57, 6, 1780-1788 (2024). doi:10.1107/S1600576724009282

  • Z. Jiang, A. Hoffmann, A. Schleife, "Influence of temperature, doping, and amorphization on the electronic structure and magnetic damping of iron", Physical Review B, 109, 23 (2024). doi:10.1103/PhysRevB.109.235147

  • K. Ito, T. Yokoi, K. Hyodo, H. Mori, "Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe", npj Computational Materials, 10, 1 (2024). doi:10.1038/s41524-024-01451-y

  • H. Wakai, A. Seko, H. Izuta, T. Nishiyama, I. Tanaka, "Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems", Physical Review B, 109, 21 (2024). doi:10.1103/PhysRevB.109.214207

  • S. Shim, J. R. Vella, J. S. Draney, D. Na, D. B. Graves, "An examination of the performance of molecular dynamics force fields: Silicon and silicon dioxide reactive ion etching", Journal of Vacuum Science & Technology A, 42, 2 (2024). doi:10.1116/6.0003425

  • Y. Liu, Y. Lei, T. Guo, A. Li, F. Tian, R. Liu, K. Shu, "Application investigations of force fields for molecular dynamic simulations of medical aluminum particles", International Journal of Computational Materials Science and Engineering, ** (2024). doi:10.1142/S2047684124500283

  • Y. Couzinié, Y. Nishiya, H. Nishi, T. Kosugi, H. Nishimori, Y. Matsushita, "Annealing for prediction of grand canonical crystal structures: Implementation of n-body atomic interactions", Physical Review A, 109, 3 (2024). doi:10.1103/PhysRevA.109.032416

  • B. A. Jasperson, H. T. Johnson, "A data-driven method for optimization of classical interatomic potentials", MRS Advances, 9, 11, 863-869 (2024). doi:10.1557/s43580-024-00802-7

  • J. W. Yoon, B. Zhou, J. Senthilnath, "SG-NNP: Species-separated Gaussian Neural Network Potential with Linear Elemental Scaling and Optimized Dimensions for Multi-component Materials", arXiv (2024). doi:10.48550/arXiv.2407.06615

  • P. D. Kolokathis, D. Zouraris, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, I. Lynch, A. Afantitis, "NanoTube Construct: A web tool for the digital construction of nanotubes of single-layer materials and the calculation of their atomistic descriptors powered by Enalos Cloud Platform", Computational and Structural Biotechnology Journal, 25, 230-242 (2024). doi:10.1016/j.csbj.2024.09.023

  • P. D. Kolokathis, D. Zouraris, E. Voyiatzis, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, K. Tämm, I. Lynch, A. Afantitis, "NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors", Computational and Structural Biotechnology Journal, 25, 81-90 (2024). doi:10.1016/j.csbj.2024.05.039

  • P. D. Kolokathis, E. Voyiatzis, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, K. Tämm, I. Lynch, A. Afantitis, "ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors", Computational and Structural Biotechnology Journal, 25, 34-46 (2024). doi:10.1016/j.csbj.2024.03.011

  • M. Lupo Pasini, M. Karabin, M. Eisenbach, "Transferring predictions of formation energy across lattices of increasing size", *Machine Learning: Science and Technology, 5, 2, 025015 (2024). doi:10.1088/2632-2153/ad3d2c

  • J. L. A. Gardner, K. T. Baker, V. L. Deringer, "Synthetic pre-training for neural-network interatomic potentials", Machine Learning: Science and Technology, 5, 1, 015003 (2024). doi:10.1088/2632-2153/ad1626

  • M. Li, G. Lu, H. Yu, M. Li, F. Zheng, "Scaling laws governing the elastic properties of 3D graphenes", Science China Technological Sciences, 67, 6, 1748-1756 (2024). doi:10.1007/s11431-023-2544-6

  • A. Siddiqui, N. D. M. Hine, "Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys", npj Computational Materials, 10, 1 (2024). doi:10.1038/s41524-024-01357-9

  • V. Venturi, R. Freitas, I. I. Abate, "Na vs. Li metal anodes for batteries: unraveling thermodynamic and electronic origins of voids and developing descriptors for artificial surface coatings", Journal of Materials Chemistry A, 12, 41, 27987-28001 (2024). doi:10.1039/d4ta00971a

  • A. Fisher, J. B. Staunton, H. Wu, P. Brommer, "First principles validation of energy barriers in Ni75Al25", Modelling and Simulation in Materials Science and Engineering, 32, 6, 065024 (2024). doi:10.1088/1361-651X/ad5c85


2023

  • X. Gong, Z. Li, A. S. L. S. Pattamatta, T. Wen, D. J. Srolovitz, "A 'Magnetic' Machine Learning Interatomic Potential for Nickel", arXiv (2023). doi:10.48550/arXiv.2312.17596

  • H. Vo, D. Frazer, A. Kohnert, S. Teysseyre, S. Fensin, P. Hosemann, "Role of low-level void swelling on plasticity and failure in a 33 dpa neutron-irradiated 304 stainless steel", International Journal of Plasticity, 164, 103577 (2023). doi:10.1016/j.ijplas.2023.103577

  • N. Tuchinda, C. A. Schuh, "The vibrational entropy spectra of grain boundary segregation in polycrystals", Acta Materialia, 245, 118630 (2023). doi:10.1016/j.actamat.2022.118630

  • S. R. Maalouf, S. S. Vel, "Nonlinear elastic behavior of 2D materials using molecular statics and comparisons with first principles calculations", Physica E: Low-dimensional Systems and Nanostructures, 148, 115633 (2023). doi:10.1016/j.physe.2022.115633

  • E. A. Antillon, C. A. Hareland, P. W. Voorhees, "Solute trapping and solute drag during non-equilibrium solidification of Fe–Cr alloys", Acta Materialia, 248, 118769 (2023). doi:10.1016/j.actamat.2023.118769

  • Y. Peng, Z. Tian, L. Liu, Q. Zheng, "Autonomous identification of Lindemann atoms based on deep learning", Materials Today Communications, 35, 106053 (2023). doi:10.1016/j.mtcomm.2023.106053

  • K. Wang, H. Shi, T. Li, L. Zhao, H. Zhai, D. Korani, J. Yeo, "Computational and data-driven modelling of solid polymer electrolytes", Digital Discovery, 2, 6, 1660-1682 (2023). doi:10.1039/d3dd00078h

  • F. Baras, O. Politano, Y. Li, V. Turlo, "A Molecular Dynamics Study of Ag-Ni Nanometric Multilayers: Thermal Behavior and Stability", Nanomaterials, 13, 14, 2134 (2023). doi:10.3390/nano13142134

  • J. Xiao, S. Li, X. Ma, J. Gao, C. Deng, Z. Wu, Y. Zhu, "Origin of Deformation Twinning in bcc Tungsten and Molybdenum", Physical Review Letters, 131, 13 (2023). doi:10.1103/PhysRevLett.131.136101

  • C. Huy Pham, H. E. Lorenzana, J. L. Belof, N. Goldman, "Hugoniot properties of porous stainless steel: Insights from molecular dynamics simulations", Journal of Applied Physics, 134, 4 (2023). doi:10.1063/5.0161413

  • M. S. Daw, M. Chandross, "Simple parameterization of embedded atom method potentials for FCC metals", Acta Materialia, 248, 118771 (2023). doi:10.1016/j.actamat.2023.118771

  • S. V. Belim, I. V. Tikhomirov, "Computer simulation of Fe epitaxial films on a Cu(100) substrate", Physica Scripta, 98, 10, 105973 (2023). doi:10.1088/1402-4896/acface

  • M. Lazar, E. Agiasofitou, "Toupin–Mindlin first strain‐gradient elasticity for cubic and isotropic materials at small scales", PAMM, 23, 3 (2023). doi:10.1002/pamm.202300121

  • R. M. Jones, K. Rossi, C. Zeni, M. Vanzan, I. Vasiljevic, A. Santana-Bonilla, F. Baletto, "Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library", Faraday Discussions, 242, 326-352 (2023). doi:10.1039/D2FD00097K

  • R. M. Jones, K. Rossi, C. Zeni, M. Vanzan, I. Vasiljevic, A. Santana-Bonilla, F. Baletto, "Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library", Faraday Discussions, 242, 326-352 (2023). doi:10.1039/d2fd00097k

  • Q. Liao, B. Wang, F. Ding, D. Li, W. Liu, L. Wang, Y. Yang, Y. Chen, "Multiscale design and application of low adhesion strength DLC release layer", Journal of Materials Research and Technology, 26, 9518-9531 (2023). doi:10.1016/j.jmrt.2023.09.247

  • M. Maździarz, "Transferability of interatomic potentials for germanene (2D germanium)", Journal of Applied Physics, 134, 18 (2023). doi:10.1063/5.0173745

  • G. Clavier, A. P. Thompson, "Computation of the thermal elastic constants for arbitrary manybody potentials in LAMMPS using the stress-fluctuation formalism", Computer Physics Communications, 286, 108674 (2023). doi:10.1016/j.cpc.2023.108674

  • F. J. Domínguez-Gutiérrez, P. Grigorev, A. Naghdi, J. Byggmästar, G. Y. Wei, T. D. Swinburne, S. Papanikolaou, M. J. Alava, "Nanoindentation of tungsten: From interatomic potentials to dislocation plasticity mechanisms", Physical Review Materials, 7, 4 (2023). doi:10.1103/PhysRevMaterials.7.043603

  • F. Pellegrini, R. Lot, Y. Shaidu, E. Küçükbenli, "PANNA 2.0: Efficient neural network interatomic potentials and new architectures", The Journal of Chemical Physics, 159, 8 (2023). doi:10.1063/5.0158075

  • M. Karabin, M. Lupo Pasini, M. Eisenbach, "ORNL_AISD_NiPt", Constellation Dataset Repository (2023). doi:10.13139/OLCF/1958172

  • J. C. Verduzco, E. Holbrook, A. Strachan, "GPT-4 as an interface between researchers and computational software: improving usability and reproducibility", arXiv (2023). doi:10.48550/arXiv.2310.11458

  • X. Du, J. K. Damewood, J. R. Lunger, R. Millan, B. Yildiz, L. Li, R. Gómez-Bombarelli, "Machine-learning-accelerated simulations to enable automatic surface reconstruction", Nature Computational Science, 3, 12, 1034-1044 (2023). doi:10.1038/s43588-023-00571-7

  • D. Olson, C. Ortner, Y. Wang, L. Zhang, "Elastic Far-Field Decay from Dislocations in Multilattices", Multiscale Modeling & Simulation, 21, 4, 1379-1409 (2023). doi:10.1137/22M1502021

  • J. Chen, H. K. Yeddu, "Study of ageing and size effects in Nickel–Titanium shape memory alloy using molecular dynamics simulations", Phase Transitions, 96, 8, 596-606 (2023). doi:10.1080/01411594.2023.2235061

  • J. Chen, J. Nokelainen, B. Barbiellini, H. K. Yeddu, "Nanoscale phenomena during wetting of copper on nickel-based superalloy: A molecular dynamics study", Computational Materials Science, 230, 112453 (2023). doi:10.1016/j.commatsci.2023.112453

  • A. Farahvash, M. Agrawal, A. A. Peterson, A. P. Willard, "Modeling Surface Vibrations and Their Role in Molecular Adsorption: A Generalized Langevin Approach", Journal of Chemical Theory and Computation, 19, 18, 6452-6460 (2023). doi:10.1021/acs.jctc.3c00473

  • S. Burlison, M. F. Becker, D. Kovar, "A molecular dynamics study of the effects of velocity and diameter on the impact behavior of zinc oxide nanoparticles", Modelling and Simulation in Materials Science and Engineering, 31, 7, 075008 (2023). doi:10.1088/1361-651X/acf060


2022

  • A. Seko, "Systematic development of polynomial machine learning potentials for metallic and alloy systems", arXiv (2022). doi:10.48550/arXiv.2209.13823

  • S. R. Taylor, "Object Storage, Persistent Memory, and Data Infrastructure for HPC Materials Informatics", arXiv (2022). doi:10.48550/arXiv.2210.07929

  • M. S. Hasan, G. Berkeley, K. Polifrone, W. Xu, "An atomistic study of deformation mechanisms in metal matrix nanocomposite materials", Materials Today Communications, 33, 104658 (2022). doi:10.1016/j.mtcomm.2022.104658

  • A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, 271, 108171 (2022). doi:10.1016/j.cpc.2021.108171

  • C. Hong, T. Chen, Z. Li, A. Du, M. Liu, P. Liu, Y. Lu, "Uniaxial ratcheting behavior and molecular dynamics simulation evaluation of 316LN stainless steel", Materials Science and Engineering: A, 849, 143535 (2022). doi:10.1016/j.msea.2022.143535

  • V. І. Kushch, "A Study of Thermodynamic and Elastic Properties of Nanosized Diamond Single Crystals by the Classical Molecular Dynamics Method", Journal of Superhard Materials, 44, 4, 229-239 (2022). doi:10.3103/S1063457622040049

  • A. Hegde, E. Weiss, W. Windl, H. Najm, C. Safta, "Bayesian calibration of interatomic potentials for binary alloys", Computational Materials Science, 214, 111660 (2022). doi:10.1016/j.commatsci.2022.111660

  • R. Polisetty, D. Mordehai, "A stochastic study of the deformation and failure strain of Au bi-crystalline nanowires with a longitudinal twin boundary", Journal of the Mechanics and Physics of Solids, 169, 105061 (2022). doi:10.1016/j.jmps.2022.105061

  • S. He, X. Zhou, D. Mordehai, J. Marian, "Thermal super-jogs control high-temperature strength in Nb-Mo-Ta-W alloys", arXiv (2022). doi:10.48550/arXiv.2205.07413

  • M. Lazar, "Reduced strain gradient elasticity model with two characteristic lengths: fundamentals and application to straight dislocations", Continuum Mechanics and Thermodynamics, 34, 6, 1433-1454 (2022). doi:10.1007/s00161-022-01128-1

  • A. Kismarahardja, Z. Wang, D. Li, L. Wang, L. Fu, Y. Chen, Z. Fan, Y. Chen, X. Han, H. Zhang, X. Liao, "Deformation-Induced Phase Transformations in Gold Nanoribbons with the 4H Phase", ACS Nano, 16, 2, 3272-3279 (2022). doi:10.1021/acsnano.1c11166

  • M. Lazar, E. Agiasofitou, T. Böhlke, "Mathematical modeling of the elastic properties of cubic crystals at small scales based on the Toupin–Mindlin anisotropic first strain gradient elasticity", Continuum Mechanics and Thermodynamics, 34, 1, 107-136 (2022). doi:10.1007/s00161-021-01050-y

  • R. Murzaev, A. Morkina, I. Tuvalev, "Dynamics of delocalized vibrational modes in bcc W: impact of interatomic potential", Laser Physics, Photonic Technologies, and Molecular Modeling, **, 25 (2022). doi:10.1117/12.2626369

  • H. E. Sauceda, L. E. Gálvez-González, S. Chmiela, L. O. Paz-Borbón, K. Müller, A. Tkatchenko, "BIGDML—Towards accurate quantum machine learning force fields for materials", Nature Communications, 13, 1 (2022). doi:10.1038/s41467-022-31093-x

  • R. Liu, Z. Wang, L. Xu, "Determining the melting temperature of three-dimensional atomic crystal using molecular dynamics simulation", 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022), **, 122 (2022). doi:10.1117/12.2635100

  • , A. Y. Morkina, I. I. Tuvalev, , S. V. Dmitriev, , Y. V. Bebikhov, , A. S. Semenov, , Y. R. Sharapova, , "The selection of interatomic potentials for simulation of extreme actions within the tungsten lattice", Frontier materials & technologies, **, 3, 16-24 (2022). doi:10.18323/2782-4039-2022-3-2-16-24

  • S. B. Lisesivdin, B. Sarikavak-Lisesivdin, "gpaw-tools – higher-level user interaction scripts for GPAW calculations and interatomic potential based structure optimization", Computational Materials Science, 204, 111201 (2022). doi:10.1016/j.commatsci.2022.111201

  • S. Starikov, D. Smirnova, T. Pradhan, I. Gordeev, R. Drautz, M. Mrovec, "Angular-dependent interatomic potential for large-scale atomistic simulation of the Fe-Cr-H ternary system", Physical Review Materials, 6, 4 (2022). doi:10.1103/PhysRevMaterials.6.043604

  • M. Hunt, S. Clark, D. Mejia, S. Desai, A. Strachan, "Sim2Ls: FAIR simulation workflows and data", PLOS ONE, 17, 3, e0264492 (2022). doi:10.1371/journal.pone.0264492

  • S. Nisany, D. Mordehai, "A Multiple Site Type Nucleation Model and Its Application to the Probabilistic Strength of Pd Nanowires", Metals, 12, 2, 280 (2022). doi:10.3390/met12020280

  • S. Homann, H. Luu, N. Merkert, "Molecular dynamics simulations of the machining of oxidized and deoxidized titanium work pieces", Results in Surfaces and Interfaces, 9, 100085 (2022). doi:10.1016/j.rsurfi.2022.100085

  • A. M. Barboza, L. C. Aliaga, D. Faria, I. N. Bastos, "Bilayer graphene kirigami", Carbon Trends, 9, 100227 (2022). doi:10.1016/j.cartre.2022.100227

  • D. Akzhigitov, T. Srymbetov, B. Golman, C. Spitas, Z. N. Utegulov, "Applied stress anisotropy effect on melting of tungsten: molecular dynamics study", Computational Materials Science, 204, 111139 (2022). doi:10.1016/j.commatsci.2021.111139

  • R. Bodlos, V. Fotopoulos, J. Spitaler, A. Shluger, L. Romaner, "Energies and structures of Cu/Nb and Cu/W interfaces from density functional theory and semi-empirical calculations", Materialia, 21, 101362 (2022). doi:10.1016/j.mtla.2022.101362


2021

  • Z. D. McClure, A. Strachan, "Expanding Materials Selection Via Transfer Learning for High-Temperature Oxide Selection", JOM, 73, 1, 103-115 (2021). doi:10.1007/s11837-020-04411-1

  • K. Zhang, L. Yin, G. Liu, "Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces", Computational Materials Science, 186, 110071 (2021). doi:10.1016/j.commatsci.2020.110071

  • M. Maździarz, "Transferability of Molecular Potentials for 2D Molybdenum Disulphide", Materials, 14, 3, 519 (2021). doi:10.3390/ma14030519

  • Z. Yang, "Speed-dependent adaptive partitioning in QM/MM MD simulations of displacement damage in solid-state systems", Physical Chemistry Chemical Physics, 23, 5, 3417-3428 (2021). doi:10.1039/d0cp05149g

  • B. Chettri, P. K. Patra, Lalmuanchhana, Lalhriatzuala, S. Verma, B. K. Rao, M. L. Verma, V. Thakur, N. Kumar, N. N. Hieu, D. P. Rai, "Induced magnetic states upon electron–hole injection at B and N sites of hexagonal boron nitride bilayer: A density functional theory study", International Journal of Quantum Chemistry, 121, 16 (2021). doi:10.1002/qua.26680

  • Y. Zeng, Y. Feng, L. Tang, K. Chen, "Effect of out-of-plane strain on the phonon structures and anharmonicity of twisted multilayer graphene", Applied Physics Letters, 118, 18 (2021). doi:10.1063/5.0047539

  • J. G. McHugh, P. Mouratidis, K. Jolley, "Ripplocations in layered materials: Sublinear scaling and basal climb", Physical Review B, 103, 19 (2021). doi:10.1103/PhysRevB.103.195436

  • P. Jochym, J. Łażewski, "High Efficiency Configuration Space Sampling – probing the distribution of available states", SciPost Physics, 10, 6 (2021). doi:10.21468/SciPostPhys.10.6.129

  • Y. Huang, M. Wang, J. Li, F. Zhu, "Removal behavior of micropipe in 4H-SiC during micromachining", Journal of Manufacturing Processes, 68, 888-897 (2021). doi:10.1016/j.jmapro.2021.06.020

  • S. Starikov, D. Smirnova, T. Pradhan, Y. Lysogorskiy, H. Chapman, M. Mrovec, R. Drautz, "Angular-dependent interatomic potential for large-scale atomistic simulation of iron: Development and comprehensive comparison with existing interatomic models", Physical Review Materials, 5, 6 (2021). doi:10.1103/PhysRevMaterials.5.063607

  • Y. Huang, M. Wang, J. Li, F. Zhu, "Effect of inclusion on 4H-SiC during nano-scratching from an atomistic perspective", Journal of Physics: Condensed Matter, 33, 43, 435402 (2021). doi:10.1088/1361-648X/ac18f2

  • J. Yoon, Z. Cao, R. K. Raju, Y. Wang, R. Burnley, A. J. Gellman, A. Barati Farimani, Z. W. Ulissi, "Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy", Machine Learning: Science and Technology, 2, 4, 045018 (2021). doi:10.1088/2632-2153/ac191c

  • N. Bertin, W. Cai, S. Aubry, V. V. Bulatov, "Core energies of dislocations in bcc metals", Physical Review Materials, 5, 2 (2021). doi:10.1103/PhysRevMaterials.5.025002

  • A. Marusczyk, S. Ramakers, M. Kappeler, P. Haremski, M. Wieler, P. Lupetin, "Atomistic Simulation of Nickel Surface and Interface Properties", High Performance Computing in Science and Engineering '19, **, 179-189 (2021). doi:10.1007/978-3-030-66792-4_13

  • Z. Wu, R. Wang, L. Zhu, S. Pattamatta, D. Srolov, "Revealing and Controlling the Core of Screw Dislocations in BCC Metals", Research Square Platform LLC (2021). doi:10.21203/rs.3.rs-879826/v1

  • Y. Shaidu, E. Küçükbenli, R. Lot, F. Pellegrini, E. Kaxiras, S. de Gironcoli, "A systematic approach to generating accurate neural network potentials: the case of carbon", npj Computational Materials, 7, 1 (2021). doi:10.1038/s41524-021-00508-6

  • J. Chen, D. Huo, H. Kumar Yeddu, "Molecular dynamics study of phase transformations in NiTi shape memory alloy embedded with precipitates", Materials Research Express, 8, 10, 106508 (2021). doi:10.1088/2053-1591/ac2b57

  • Z. Yang, "Speed-dependent adaptive partitioning in QM/MM MD simulations of displacement damage in solid-state systems", Physical Chemistry Chemical Physics, 23, 5, 3417-3428 (2021). doi:10.1039/D0CP05149G

  • Y. Lysogorskiy, C. v. d. Oord, A. Bochkarev, S. Menon, M. Rinaldi, T. Hammerschmidt, M. Mrovec, A. Thompson, G. Csányi, C. Ortner, R. Drautz, "Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon", npj Computational Materials, 7, 1 (2021). doi:10.1038/s41524-021-00559-9

  • I. M. Padilla Espinosa, T. D. B. Jacobs, A. Martini, "Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms", Journal of Chemical Theory and Computation, 17, 7, 4486-4498 (2021). doi:10.1021/acs.jctc.1c00434


2020

  • A. Seko, "Machine Learning Potential Repository", arXiv (2020). doi:10.48550/arXiv.2007.14206

  • G. Guttormsen, A. C. Fletcher, M. M. Oppenheim, "Atomic‐scale simulations of meteor ablation", Journal of Geophysical Research: Space Physics, 125, 9 (2020). doi:10.1029/2020JA028229

  • S. Orihara, Y. Shibuta, T. Mohri, "Molecular dynamics simulation of nucleation from undercooled melt of nickel–aluminum alloy and discussion on polymorphism in nucleation", Materials Transactions, 61, 4, 750-757 (2020). doi:10.2320/matertrans.MT-M2019353

  • V. Jusuf, R. S. Elliott, "A framework For the interpretation of modulated martensites in shape memory alloys", Journal of the Mechanics and Physics of Solids, 138, 103879 (2020). doi:10.1016/j.jmps.2020.103879

  • H. Niu, L. Bonati, P. M. Piaggi, M. Parrinello, "Ab initio phase diagram and nucleation of gallium", Nature Communications, 11, 1 (2020). doi:10.1038/s41467-020-16372-9

  • S. Sakout, D. Weisz-Patrault, A. Ehrlacher, "Energetic upscaling strategy for grain growth. I: Fast mesoscopic model based on dissipation", Acta Materialia, 196, 261-279 (2020). doi:10.1016/j.actamat.2020.06.032

  • E. Garijo del Río, S. Kaappa, J. A. Garrido Torres, T. Bligaard, K. W. Jacobsen, "Machine learning with bond information for local structure optimizations in surface science", The Journal of Chemical Physics, 153, 23 (2020). doi:10.1063/5.0033778

  • G. Po, N. C. Admal, B. Svendsen, "Non-local Thermoelasticity Based on Equilibrium Statistical Thermodynamics", Journal of Elasticity, 139, 1, 37-59 (2020). doi:10.1007/s10659-019-09745-9

  • Z. Yang, "Speed-Dependent Adaptive Partitioning QM/MM for Displacement Damage Simulations", American Chemical Society (ACS) (2020). doi:10.26434/chemrxiv.13019117.v1

  • M. Hodapp, A. Shapeev, "In operando active learning of interatomic interaction during large-scale simulations", Machine Learning: Science and Technology, 1, 4, 045005 (2020). doi:10.1088/2632-2153/aba373

  • Z. Yang, "On-the-fly determination of active region centers in adaptive-partitioning QM/MM", Physical Chemistry Chemical Physics, 22, 34, 19307-19317 (2020). doi:10.1039/d0cp03034a

  • S. Desai, S. T. Reeve, K. G. Vishnu, A. Strachan, "Tuning martensitic transformations via coherent second phases in nanolaminates using free energy landscape engineering", Journal of Applied Physics, 127, 12 (2020). doi:10.1063/1.5145008

  • A. Kavalur, V. Guduguntla, W. K. Kim, "Effects of Langevin friction and time steps in the molecular dynamics simulation of nanoindentation", Molecular Simulation, 46, 12, 911-922 (2020). doi:10.1080/08927022.2020.1791858

  • L. Li, H. Li, I. D. Seymour, L. Koziol, G. Henkelman, "Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials", The Journal of Chemical Physics, 152, 22 (2020). doi:10.1063/5.0007391

  • M. Kappeler, A. Marusczyk, B. Ziebarth, "Simulation of nickel surfaces using ab-initio and empirical methods", Materialia, 12, 100675 (2020). doi:10.1016/j.mtla.2020.100675

  • R. Lot, F. Pellegrini, Y. Shaidu, E. Küçükbenli, "PANNA: Properties from Artificial Neural Network Architectures", Computer Physics Communications, 256, 107402 (2020). doi:10.1016/j.cpc.2020.107402

  • K. Choudhary, K. F. Garrity, A. C. E. Reid, B. DeCost, A. J. Biacchi, A. R. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A. G. Kusne, A. Centrone, A. Davydov, J. Jiang, R. Pachter, G. Cheon, E. Reed, A. Agrawal, X. Qian, V. Sharma, H. Zhuang, S. V. Kalinin, B. G. Sumpter, G. Pilania, P. Acar, S. Mandal, K. Haule, D. Vanderbilt, K. Rabe, F. Tavazza, "The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design", npj Computational Materials, 6, 1 (2020). doi:10.1038/s41524-020-00440-1

  • D. Akzhigitov, T. Srymbetov, B. Golman, C. Spitas, Z. N. Utegulov, "Melting of tungsten under uniaxial and shear stresses: molecular dynamics simulation", Modelling and Simulation in Materials Science and Engineering, 28, 7, 075008 (2020). doi:10.1088/1361-651X/abaf39


2019

  • T. Hammerschmidt, B. Seiser, M. Ford, A. Ladines, S. Schreiber, N. Wang, J. Jenke, Y. Lysogorskiy, C. Teijeiro, M. Mrovec, M. Cak, E. Margine, D. Pettifor, R. Drautz, "BOPfox program for tight-binding and analytic bond-order potential calculations", Computer Physics Communications, 235, 221-233 (2019). doi:10.1016/j.cpc.2018.08.013

  • G. A. Almyras, D. G. Sangiovanni, K. Sarakinos, "Semi-Empirical Force-Field Model for the Ti1−xAlxN  (0 ≤ x ≤ 1) System", Materials, 12, 2, 215 (2019). doi:10.3390/ma12020215

  • P. Schlexer, A. B. Andersen, B. Sebok, I. Chorkendorff, J. Schiøtz, T. W. Hansen, "Size‐Dependence of the Melting Temperature of Individual Au Nanoparticles", Particle & Particle Systems Characterization, 36, 3 (2019). doi:10.1002/ppsc.201800480

  • G. Po, N. C. Admal, M. Lazar, "The Green tensor of Mindlin’s anisotropic first strain gradient elasticity", Materials Theory, 3, 1 (2019). doi:10.1186/s41313-019-0015-2

  • Y. Lysogorskiy, T. Hammerschmidt, J. Janssen, J. Neugebauer, R. Drautz, "Transferability of interatomic potentials for molybdenum and silicon", Modelling and Simulation in Materials Science and Engineering, 27, 2, 025007 (2019). doi:10.1088/1361-651X/aafd13

  • S. Longbottom, P. Brommer, "Uncertainty quantification for classical effective potentials: an extension to potfit", Modelling and Simulation in Materials Science and Engineering, 27, 4, 044001 (2019). doi:10.1088/1361-651X/ab0d75

  • J. Roth, E. Eisfeld, D. Klein, S. Hocker, H. Lipp, H. Trebin, "IMD – the ITAP molecular dynamics simulation package", The European Physical Journal Special Topics, 227, 14, 1831-1836 (2019). doi:10.1140/epjst/e2019-800147-7

  • J. Janssen, S. Surendralal, Y. Lysogorskiy, M. Todorova, T. Hickel, R. Drautz, J. Neugebauer, "pyiron: An integrated development environment for computational materials science", Computational Materials Science, 163, 24-36 (2019). doi:10.1016/j.commatsci.2018.07.043

  • M. Höhnerbach, P. Bientinesi, "Accelerating AIREBO: Navigating the Journey from Legacy to High‐Performance Code", Journal of Computational Chemistry, 40, 14, 1471-1482 (2019). doi:10.1002/jcc.25796

  • S. T. Reeve, D. M. Guzman, L. Alzate-Vargas, B. Haley, P. Liao, A. Strachan, "Online simulation powered learning modules for materials science", MRS Advances, 4, 50, 2727-2742 (2019). doi:10.1557/adv.2019.287

  • L. Zhang, Y. Shibuta, X. Huang, C. Lu, M. Liu, "Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective", Computational Materials Science, 156, 421-433 (2019). doi:10.1016/j.commatsci.2018.10.021

  • C. de Tomas, A. Aghajamali, J. L. Jones, D. J. Lim, M. J. López, I. Suarez-Martinez, N. A. Marks, "Transferability in interatomic potentials for carbon", Carbon, 155, 624-634 (2019). doi:10.1016/j.carbon.2019.07.074


2018

  • M. A. Tschopp, B. Chris Rinderspacher, S. Nouranian, M. I. Baskes, S. R. Gwaltney, M. F. Horstemeyer, "Quantifying parameter sensitivity and uncertainty for interatomic potential design: Application to saturated hydrocarbons", ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4, 1 (2018). doi:10.1115/1.4037455

  • S. Papanikolaou, "Learning local, quenched disorder in plasticity and other crackling noise phenomena", npj Computational Materials, 4, 1 (2018). doi:10.1038/s41524-018-0083-x

  • K. Choudhary, A. J. Biacchi, S. Ghosh, L. Hale, A. R. H. Walker, F. Tavazza, "High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields", Journal of Physics: Condensed Matter, 30, 39, 395901 (2018). doi:10.1088/1361-648X/aadaff

  • B. Onat, E. D. Cubuk, B. D. Malone, E. Kaxiras, "Implanted neural network potentials: Application to Li-Si alloys", Physical Review B, 97, 9 (2018). doi:10.1103/PhysRevB.97.094106

  • A. P. Bartók, J. Kermode, N. Bernstein, G. Csányi, "Machine Learning a General-Purpose Interatomic Potential for Silicon", Physical Review X, 8, 4 (2018). doi:10.1103/PhysRevX.8.041048


2017

  • D. B. Brough, D. Wheeler, S. R. Kalidindi, "Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials", Integrating Materials and Manufacturing Innovation, 6, 1, 36-53 (2017). doi:10.1007/s40192-017-0089-0

  • S. Hajinazar, J. Shao, A. N. Kolmogorov, "Stratified construction of neural network based interatomic models for multicomponent materials", Physical Review B, 95, 1 (2017). doi:10.1103/PhysRevB.95.014114

  • A. Stukowski, E. Fransson, M. Mock, P. Erhart, "Atomicrex—a general purpose tool for the construction of atomic interaction models", Modelling and Simulation in Materials Science and Engineering, 25, 5, 055003 (2017). doi:10.1088/1361-651X/aa6ecf

  • I. A. Solov’yov, A. V. Korol and A. V. Solov’yov, "Novel and Emerging Technologies", in Multiscale Modeling of Complex Molecular Structure and Dynamics with MBN Explorer. Springer, Cham (2017). doi:10.1007/978-3-319-56087-8_10

  • A. Takahashi, A. Seko, I. Tanaka, "Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium", Physical Review Materials, 1, 6 (2017). doi:10.1103/PhysRevMaterials.1.063801

  • J. S. Gibson, S. G. Srinivasan, M. I. Baskes, R. E. Miller, A. K. Wilson, "A multi-state modified embedded atom method potential for titanium", Modelling and Simulation in Materials Science and Engineering, 25, 1, 015010 (2017). doi:10.1088/1361-651X/25/1/015010

  • A. Hjorth Larsen, J. Jørgen Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dułak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. Bjerre Jensen, J. Kermode, J. R. Kitchin, E. Leonhard Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. Bergmann Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K. S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, K. W. Jacobsen, "The atomic simulation environment—a Python library for working with atoms", Journal of Physics: Condensed Matter, 29, 27, 273002 (2017). doi:10.1088/1361-648X/aa680e

  • K. Kim, B. Lee, "Modified embedded-atom method interatomic potentials for Mg-Nd and Mg-Pb binary systems", Calphad, 57, 55-61 (2017). doi:10.1016/j.calphad.2017.03.003

  • K. Choudhary, F. Y. P. Congo, T. Liang, C. Becker, R. G. Hennig, F. Tavazza, "Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface", Scientific Data, 4, 1 (2017). doi:10.1038/sdata.2016.125

  • N. C. Admal, J. Marian, G. Po, "The atomistic representation of first strain-gradient elastic tensors", Journal of the Mechanics and Physics of Solids, 99, 93-115 (2017). doi:10.1016/j.jmps.2016.11.005

  • J. Cho, J. Molinari, G. Anciaux, "Mobility law of dislocations with several character angles and temperatures in FCC aluminum", International Journal of Plasticity, 90, 66-75 (2017). doi:10.1016/j.ijplas.2016.12.004


2016

  • Jones, R.E., Weinberger, C.R., Coleman, S.P., Tucker, G.J., Introduction to Atomistic Simulation Methods, in C. Weinberger, G. Tucker (eds), Multiscale Materials Modeling for Nanomechanics. Springer Series in Materials Science, vol 245, pp. 1-52. Springer, Cham (2016). doi:10.1007/978-3-319-33480-6_1

  • S. P. Coleman, M. A. Tschopp, C. R. Weinberger, D. E. Spearot, "Bridging atomistic simulations and experiments via virtual diffraction: understanding homophase grain boundary and heterophase interface structures", Journal of Materials Science, 51, 3, 1251-1260 (2016). doi:10.1007/s10853-015-9087-9

  • K. Sebeck, C. Shao, J. Kieffer, "Alkane–metal interfacial Structure and elastic properties by molecular dynamics simulation", ACS Applied Materials & Interfaces, 8, 26, 16885-16896 (2016). doi:10.1021/acsami.6b01665

  • P. Mereghetti, G. Maccari, G. L. B. Spampinato, V. Tozzini, "Optimization of analytical potentials for coarse-grained biopolymer models", The Journal of Physical Chemistry B, 120, 33, 8571-8579 (2016). doi:10.1021/acs.jpcb.6b02555

  • G. B. Sushko, I. A. Solov’yov, A. V. Solov’yov, "Molecular dynamics for irradiation driven chemistry: application to the FEBID process", *The European Physical Journal D, 70, 10 (2016). doi:10.1140/epjd/e2016-70283-5

  • Y. Yu, B. Wang, M. Wang, G. Sant, M. Bauchy, "Revisiting silica with ReaxFF: Towards improved predictions of glass structure and properties via reactive molecular dynamics", Journal of Non-Crystalline Solids, 443, 148-154 (2016). doi:10.1016/j.jnoncrysol.2016.03.026


2015

  • J. R. Lile, S. Zhou, "Theoretical modeling of the PEMFC catalyst layer: A review of atomistic methods", Electrochimica Acta, 177, 4-20 (2015). doi:10.1016/j.electacta.2015.01.136

  • T. C. O’Connor, J. Andzelm, M. O. Robbins, "AIREBO-M: A reactive model for hydrocarbons at extreme pressures", The Journal of Chemical Physics, 142, 2 (2015). doi:10.1063/1.4905549

  • Z. T. Trautt, F. Tavazza, C. A. Becker, "Facilitating the selection and creation of accurate interatomic potentials with robust tools and characterization", Modelling and Simulation in Materials Science and Engineering, 23, 7, 074009 (2015). doi:10.1088/0965-0393/23/7/074009

  • P. Brommer, A. Kiselev, D. Schopf, P. Beck, J. Roth, H. Trebin, "Classical interaction potentials for diverse materials fromab initiodata: a review ofpotfit", Modelling and Simulation in Materials Science and Engineering, 23, 7, 074002 (2015). doi:10.1088/0965-0393/23/7/074002


2014

  • G. Maccari, G. L. Spampinato, V. Tozzini, "SecStAnT: secondary structure analysis tool for data selection, statistics and models building", Bioinformatics, 30, 5, 668-674 (2014). doi:10.1093/bioinformatics/btt586

  • G. L. B. Spampinato, G. Maccari, V. Tozzini, "Minimalist model for the dynamics of helical polypeptides: A statistic-based parametrization", Journal of Chemical Theory and Computation, 10, 9, 3885-3895 (2014). doi:10.1021/ct5004059


2013

  • C. A. Becker, F. Tavazza, Z. T. Trautt, R. A. Buarque de Macedo, "Considerations for choosing and using force fields and interatomic potentials in materials science and engineering", Current Opinion in Solid State and Materials Science, 17, 6, 277-283 (2013). doi:10.1016/j.cossms.2013.10.001


2012

  • M. Tschopp, K. Solanki, M. Baskes, F. Gao, X. Sun, M. Horstemeyer, "Generalized framework for interatomic potential design: Application to Fe–He system", Journal of Nuclear Materials, 425, 1-3, 22-32 (2012). doi:10.1016/j.jnucmat.2011.08.003