18 citations · 33 across the 7 of their papers we have counts for
8 papers
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…
Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4
Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and datasets have led to increasingly acc…
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…
Exploring Charge Density Waves in two-dimensional NbSe2 with Machine Learning
Norma Rivano, Francesco Libbi, Chuin Wei Tan +8
Niobium diselenide (NbSe) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, r…
Unified Differentiable Learning of Electric Response
Stefano Falletta, Andrea Cepellotti, Anders Johansson +4
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…
Towards Sustainable Ultrawide Bandgap Van der Waals Materials: An ab initio Screening Effort
Chuin Wei Tan, Linqiang Xu, Chen Chen Er +6
The sustainable development of next-generation device technology is paramount in the face of climate change and the looming energy crisis. Tremendous efforts have been made in the…
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…