872 citations · 2.9k across the 35 of their papers we have counts for
3 papers · 1 filter
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…
Predictive Scale-Bridging Simulations through Active Learning
Satish Karra, Mohamed Mehana, Nicholas Lubbers +12
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-b…
Training Data Selection for Accuracy and Transferability of Interatomic Potentials
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers +3
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, lin…