The DOI matches its Crossref record; 130 of the 130 references it cites were found in a registry or an index.
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
Abstract
Modern simulation techniques have reached a level of maturity which allows a wide range of problems in chemistry and materials science to be addressed. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and despite the rapid evolution of computer hardware no fundamental change in this situation can be expected. Consequently, the development of more efficient but equally reliable atomistic potentials to reach an atomic level understanding of complex systems has received considerable attention in recent years. A promising new development has been the introduction of machine learning (ML) methods to describe the atomic interactions. Once trained with electronic structure data, ML potentials can accelerate computer simulations by several orders of magnitude, while preserving quantum mechanical accuracy. This Review considers the methodology of an important class of ML potentials that employs artificial neural networks.
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Cited by
Machine learning for molecular and materials science
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Self-citation
Metadata
- Authors
- Behler, J.
- Venue
- Angewandte Chemie International Edition
- Publisher
- Wiley
- Published
- 2017-08-18
- Volume
- 56
- Issue
- 42
- Pages
- 12828-12840
- ISSN
- 1433-7851 (ISSN-L)
- 1521-3773
- OpenAlex
- W2746244909
- Cited by
- 707
- References
- 130
- FWCI
- 25.75
- Type
- journal article
- Language
- en
- Open access
- closed
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