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Accelerating Detergent Innovation with Computational Chemistry

Accelerating Detergent Innovation with Computational Chemistry Chemical R&D has long relied on an experimental trial-and-error approach. Historically, the development of new products has required extensive timeframes due to the difficulty of synthesizing new compounds and of testing certain properties in the laboratory, creating significant bottlenecks. However, the industry is starting to embrace more advanced methodologies,

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Ortin2025 Nextmol 10 Detergent Innovation Figure5c
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Publications

Marti2024 Nextmol 09 Biobase Polymers Supervisedml

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

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Mohr2023 Nextmol 08 Enthalpy Of Dissociation Abstract

Enthalpy of dissociation of the mixed methane + propane sII hydrate along the three-phase equilibrium line

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Petuya2023 Nextmol 07 Asphaltene Inhibitors Fig9

Molecular Dynamics Simulations of Asphaltene Aggregation

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Mohr2022 Nextmol Adsorption Hydrates Anti Agglomerants H

Assessing the effect of a liquid water layer on the adsorption of hydrate antiagglomerants using molecular simulations

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Navarro2021 Nextmol 04 Organic Friction Modifiers Pmf Sa V2

Molecular Simulations of Surfactant Adsorption on Iron Oxide from Hydrocarbon Solvents

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Mohr2021 Nextmol 02 Size Dependence Stabilitymap

Size dependence of the dissociation process of spherical hydrate particles via microsecond MD simulations

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Mohr2021 Nextmol Molecular Modeling Anti Agglomerants V

Ranking the Efficiency of Gas Hydrate Anti-agglomerants through Molecular Dynamic Simulations

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Nextmol awarded the EIC Accelerator project “The Computational Laboratory for the Next Generation Sustainable Chemicals and Digital Transition”

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