Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynami...

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Autores principales:	Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1014ecc724a64101ba64cd3dbf9a3690
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spelling	oai:doaj.org-article:1014ecc724a64101ba64cd3dbf9a36902021-12-02T16:35:07ZDifferentiable sampling of molecular geometries with uncertainty-based adversarial attacks10.1038/s41467-021-25342-82041-1723https://doaj.org/article/1014ecc724a64101ba64cd3dbf9a36902021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25342-8https://doaj.org/toc/2041-1723Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynamics simulations.Daniel Schwalbe-KodaAik Rui TanRafael Gómez-BombarelliNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
description	Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynamics simulations.
format	article
author	Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli
author_facet	Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli
author_sort	Daniel Schwalbe-Koda
title	Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
title_short	Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
title_full	Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
title_fullStr	Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
title_full_unstemmed	Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
title_sort	differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1014ecc724a64101ba64cd3dbf9a3690
work_keys_str_mv	AT danielschwalbekoda differentiablesamplingofmoleculargeometrieswithuncertaintybasedadversarialattacks AT aikruitan differentiablesamplingofmoleculargeometrieswithuncertaintybasedadversarialattacks AT rafaelgomezbombarelli differentiablesamplingofmoleculargeometrieswithuncertaintybasedadversarialattacks
_version_	1718383734827253760

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

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