DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference-Reference-Cited by-同舟云学术

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DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference

Published:2024-05-01 Issue:9 Volume:20 Page:4007-4018
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Guibourg Paul¹,Dontot Léo¹,Anglade Pierre-Matthieu¹^ORCID,Gervais Benoit¹^ORCID

Affiliation:

1. Laboratoire Cimap, UMR6252─Université de Caen Normandie, École Nationale Supérieure d’Ingénieures de Caen, Commissariat à l’Énergie Atomique, Centre National de la Recherche Scientifique, 6 Boulevard Du Maréchal Juin, 14050 Caen Cedex, France

Funder

R?gion Normandie

Labex EMC3

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00107

Reference53 articles.

1. Finding Density Functionals with Machine Learning

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

3. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer

4. Quantum-chemical insights from deep tensor neural networks

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

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