Spatially Resolved Uncertainties for Machine Learning Potentials

Author:

Heid Esther1ORCID,Schörghuber Johannes1ORCID,Wanzenböck Ralf1ORCID,Madsen Georg K. H.1ORCID

Affiliation:

1. Institute of Materials Chemistry, TU Wien, A-1060 Vienna, Austria

Funder

Austrian Science Fund

Publisher

American Chemical Society (ACS)

Reference53 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. A Differentiable Neural-Network Force Field for Ionic Liquids

3. SchNet – A deep learning architecture for molecules and materials

4. Batatia, I.; Kovacs, D. P.; Simm, G.; Ortner, C.; Csányi, G. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. In Advances in Neural Information Processing Systems, 2022; Vol. 35, pp 11423–11436.

5. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

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