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
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Acceso en línea:https://doaj.org/article/1014ecc724a64101ba64cd3dbf9a3690
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