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Perspective on integrating machine learning into computational chemistry and materials science
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Westermayr, Julia, Gastegger, Michael, Schütt, Kristoff T. and Maurer, Reinhard J. (2021) Perspective on integrating machine learning into computational chemistry and materials science. The Journal of Chemical Physics, 154 (23). 230903. doi:10.1063/5.0047760
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Official URL: https://doi.org/10.1063/5.0047760
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties – be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QC Physics Q Science > QD Chemistry |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Science > Chemistry > Computational and Theoretical Chemistry Centre |
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Library of Congress Subject Headings (LCSH): | Machine learning, Computational chemistry , Materials science, Materials science -- Computer simulation, Electronic structure, Electronic structure -- Computer simulation, Quantum chemistry , Quantum chemistry -- Computer programs , Artificial intelligence , Molecular dynamics -- Simulation methods | ||||||||||||
Journal or Publication Title: | The Journal of Chemical Physics | ||||||||||||
Publisher: | AIP Publishing | ||||||||||||
Official Date: | 2021 | ||||||||||||
Dates: |
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Volume: | 154 | ||||||||||||
Number: | 23 | ||||||||||||
Article Number: | 230903 | ||||||||||||
DOI: | 10.1063/5.0047760 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Reuse Statement (publisher, data, author rights): | This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Julia Westermayr, Michael Gastegger, Kristof T. Schütt, and Reinhard J. Maurer, "Perspective on integrating machine learning into computational chemistry and materials science", The Journal of Chemical Physics 154, 230903 (2021). and may be found at https://doi.org/10.1063/5.0047760 | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 22 June 2021 | ||||||||||||
Date of first compliant Open Access: | 23 June 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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