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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…
physics.comp-ph2022
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling
Yonatan Kurniawan, Cody L. Petrie, Mark K. Transtrum +4
Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model's predictions…