11 citations · 15 across the 5 of their papers we have counts for
3 papers · 1 filter
BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials
Laura Zichi, Gil Harari, Chuin Wei Tan +6
Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expens…
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…
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…