18 citations · 33 across the 7 of their papers we have counts for
4 papers · 1 filter
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large a…
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…
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…
Automatic Differentiation for Orbital-Free Density Functional Theory
Chuin Wei Tan, Chris J. Pickard, William C. Witt
Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, acceler…