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cs.LG2025
Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields
Hong Sun, Joshua A. Vita, Amit Samanta +1
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and mat…
cs.LG2024
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…