From the 1 of 5 linked papers with an AI index.
5 papers
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…
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…
HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class
James V. Roggeveen, Erik Y. Wang, Will Flintoft +42
Large language models (LLMs) have shown remarkable progress in mathematical problem-solving, but evaluation has largely focused on problems that have exact analytical solutions or…
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…