The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Quantum chemical accuracy from density functional approximations via machine learning
Open accessAbstract
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol −1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol −1 ) on test data. Moreover, density-based Δ -learning (learning only the correction to a standard DFT calculation, termed Δ -DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ -DFT is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C 6 H 4 (OH) 2 ) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ -DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.
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Cited by
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Self-citationGeometric deep learning on molecular representations
Atz, K., Grisoni, F., & Schneider, G. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00418-8 · Cited by 373
Exploring chemical compound space with quantum-based machine learning
von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0189-9 · Cited by 369
Self-citationMachine learning in materials science: From explainable predictions to autonomous design
Pilania, G. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110360 · Cited by 281
Metadata
- Authors
- Bogojeski, M., Vogt-Maranto, L., Tuckerman, M. E., Müller, K.-R., & Burke, K.
- Venue
- Nature Communications
- Published
- 2020-10-16
- Volume
- 11
- Issue
- 1
- Pages
- 5223-5223
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3093036756
- Cited by
- 384
- References
- 113
- FWCI
- 19.69
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bogojeski, M., Vogt-Maranto, L., Tuckerman, M. E., Müller, K.-R., & Burke, K. (2020). Quantum chemical accuracy from density functional approximations via machine learning. Nature Communications. https://doi.org/10.1038/s41467-020-19093-1
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