872 citations · 1.1k across the 13 of their papers we have counts for
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physics.chem-ph2022★ 26 cited
Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
Michael Chigaev, Justin S. Smith, Steven Anaya +4
Atomistic machine learning focuses on the creation of models which obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invarianc…
physics.chem-ph2022
Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights
Aaron Philip, Guoqing Zhou, Benjamin Nebgen
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not cle…