8 citations · 15 across the 10 of their papers we have counts for
5 papers · 1 filter
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to…
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
Weishi Wang, Mark K. Transtrum, Vincenzo Lordi +2
An adaptive physics-inspired model design strategy for machine-learning interatomic potentials (MLIPs) is proposed. This strategy relies on iterative reconfigurations of composite…
Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends
Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta +3
Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs)…
Cross-scale covariance for material property prediction
Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta +4
A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of predicti…