21 citations · 21 across the 5 of their papers we have counts for
5 papers
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…
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…
Spline-based neural network interatomic potentials: blending classical and machine learning models
Joshua A. Vita, Dallas R. Trinkle
While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained,…
ColabFit Exchange: open-access datasets for data-driven interatomic potentials
Joshua A. Vita, Eric G. Fuemmeler, Amit Gupta +5
Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational mate…
Data efficiency and extrapolation trends in neural network interatomic potentials
Joshua A. Vita, Daniel Schwalbe-Koda
Over the last few years, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivarian…