Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach-Reference-Cited by-同舟云学术

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Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach

Published:2024-05-06 Issue:10 Volume:64 Page:4047-4058
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Grassano Juan S.¹²^ORCID,Pickering Ignacio³^ORCID,Roitberg Adrian E.²³^ORCID,González Lebrero Mariano C.¹²^ORCID,Estrin Dario A.¹²^ORCID,Semelak Jonathan A.¹²^ORCID

Affiliation:

1. Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física, Universidad de Buenos Aires, Intendente Güiraldes 2160, Buenos Aires C1428EHA, Argentina

2. CONICET─Universidad de Buenos Aires, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía (INQUIMAE), Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EHA, Argentina

3. Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States

Funder

Consejo Nacional de Investigaciones Cient?ficas y T?cnicas

Office of Advanced Cyberinfrastructure

Universidad de Buenos Aires

Agencia Nacional de Promoci?n de la Investigaci?n, el Desarrollo Tecnol?gico y la Innovaci?n

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00478

Reference80 articles.

1. Machine Learning for Chemical Reactions

2. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

3. Quantum Chemistry in the Age of Machine Learning

4. Deep learning for computational chemistry

5. Chapter 8 Machine Learning in Computational Chemistry

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