Nima Shoghi
Member of Technical Staff, Boltz
London, UK
I'm a Member of Technical Staff at Boltz, where I work on the next generation of biomolecular foundation models. My research focuses on AI for science, spanning biology, chemistry, drug discovery, and materials science; previously, I worked on these areas at Georgia Tech, Meta FAIR, and ByteDance Seed.
Selected Publications
Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics
Nima Shoghi, Yuxuan Liu, Yuning Shen, Rob Brekelmans, Pan Li, and Quanquan Gu
Introduces STAR-MD, a scalable SE(3)-equivariant diffusion model that generates physically plausible protein trajectories over microsecond timescales using a causal diffusion transformer with joint spatiotemporal attention.
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John Kitchin, Zachary Ulissi, C. Lawrence Zitnick, and Brandon Wood
Introduces Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that leverages diverse data to advance atomic property prediction across chemical domains, achieving state-of-the-art performance on 34 out of 40 downstream tasks.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Félix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, and C. Lawrence Zitnick
Introduces the Open Catalyst 2022 (OC22) dataset, consisting of 62,331 DFT relaxations, to accelerate machine learning for oxide electrocatalysts and establish benchmarks for the field.
Lingyu Kong, Nima Shoghi, Guoxiang Hu, Pan Li, and Victor Fung
Introduces MatterTune, a modular platform that enables fine-tuning of pre-trained atomistic foundation models for materials science applications.
