5 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 5 cited
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
Zeren Shui, Daniel S. Karls, Mingjian Wen +3
For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurat…
cs.DC2022
HPC Extensions to the OpenKIM Processing Pipeline
Daniel S. Karls, Steven M. Clark, Brendon A. Waters +2
The Open Knowledgebase of Interatomic Models (OpenKIM) is an NSF Science Gateway that archives fully functional computer implementations of interatomic models (potentials and force…