Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Name
s41467-021-25342-8.pdf
Description
Published version
Size
3.41 MB
Format
Adobe PDF
Checksum (MD5)
bf902ed316963006944837414ac4c4c8
Author(s)
Schwalbe-Koda, Daniel
Tan, Aik Rui
Gómez-Bombarelli, Rafael
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Schwalbe-Koda, Daniel, Tan, Aik Rui and Gómez-Bombarelli, Rafael. 2021. "Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractNeural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-021-25342-8
