Data efficiency and extrapolation trends in neural network interatomic potentials

Data efficiency and extrapolation trends in neural network interatomic potentials

Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still consider...

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Bibliographic Details
Main Authors: Joshua A Vita, Daniel Schwalbe-Koda
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
neural network potentials
extrapolation
loss landscapes
graph neural networks
machine learning potentials
atomistic simulations
Online Access:https://doi.org/10.1088/2632-2153/acf115

Internet

https://doi.org/10.1088/2632-2153/acf115

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