Extending machine learning beyond interatomic potentials for predicting molecular properties.
Journal
Nature reviews. Chemistry
ISSN: 2397-3358
Titre abrégé: Nat Rev Chem
Pays: England
ID NLM: 101703631
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
accepted:
15
07
2022
medline:
29
4
2023
pubmed:
29
4
2023
entrez:
28
4
2023
Statut:
ppublish
Résumé
Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.
Identifiants
pubmed: 37117713
doi: 10.1038/s41570-022-00416-3
pii: 10.1038/s41570-022-00416-3
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
653-672Informations de copyright
© 2022. Springer Nature Limited.
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