36 citations · 165 across the 12 of their papers we have counts for
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cond-mat.mtrl-sci2021★ 24 cited
Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams +5
In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and…
cond-mat.mtrl-sci2021★ 27 cited
KLIFF: A framework to develop physics-based and machine learning interatomic potentials
Mingjian Wen, Yaser Afshar, Ryan S. Elliott +1
Interatomic potentials (IPs) are reduced-order models for calculating the potential energy of a system of atoms given their positions in space and species. IPs treat atoms as class…