29 citations · 44 across the 4 of their papers we have counts for
4 papers
Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics
Kaihua Ji, Luning Sun, Shusen Liu +2
Microstructural pattern formation, such as dendrite growth, occurs widely in materials and energy systems, significantly influencing material properties and functional performance.…
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…
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh +2
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification…
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields
Joshua A. Vita, Amit Samanta, Fei Zhou +1
Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computatio…