works on
equivariant neural networks 1foundation models 1machine-learned interatomic potentials 1materials discovery 1thermal conductivity prediction 1

From the 1 of 3 linked papers with an AI index.

collaborators

3 papers

physics.comp-ph2026

Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials

Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12

The paper presents fast and accurate equivariant machine‑learned interatomic potentials (NequIP and Allegro) as foundation models that scale to ultra‑large datasets while maintaini…

physics.comp-ph2026

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9

First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning…

physics.comp-ph2025

High-performance training and inference for deep equivariant interatomic potentials

Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11

Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…