DOI: 10.1063/1.5009502. Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

10.1063/1.5009502

Crossref journal-article

AIP Publishing

The Journal of Chemical Physics (317)

Abstract

Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions—electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters—optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically relevant molecules. We further focus on hydrogen-bonded complexes—essential but challenging due to their directional nature—where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML for denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal.

Bibliography

Bereau, T., DiStasio, R. A., Tkatchenko, A., & von Lilienfeld, O. A. (2018). Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning. The Journal of Chemical Physics, 148(24).

Authors 4

Tristan Bereau (first)
Robert A. DiStasio (additional)
Alexandre Tkatchenko (additional)
O. Anatole von Lilienfeld (additional)

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Dates

Type	When
Created	7 years, 5 months ago (March 15, 2018, 11:34 a.m.)
Deposited	2 years ago (Aug. 2, 2023, 5:48 p.m.)
Indexed	2 weeks, 2 days ago (Aug. 6, 2025, 9:03 a.m.)
Issued	7 years, 5 months ago (March 15, 2018)
Published	7 years, 5 months ago (March 15, 2018)
Published Online	7 years, 5 months ago (March 15, 2018)
Published Print	7 years, 1 month ago (June 28, 2018)

Funders 4

National Science Foundation 10.13039/100000001
Region: Americas
gov (National government)
Labels4
1. U.S. National Science Foundation
2. NSF
3. US NSF
4. USA NSF
Awards1
1. DMR-1719875
Deutsche Forschungsgemeinschaft 10.13039/501100001659
Region: Europe
gov (National government)
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1. German Research Association
2. German Research Foundation
3. DFG
Awards1
1. Emmy Noether
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10.13039/501100001711
Region: Europe
pri (Trusts, charities, foundations (both public and private))
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1. Schweizerischer Nationalfonds
2. Swiss National Science Foundation
3. Fonds National Suisse de la Recherche Scientifique
4. Fondo Nazionale Svizzero per la Ricerca Scientifica
5. Fonds National Suisse
6. Fondo Nazionale Svizzero
7. Schweizerische Nationalfonds
8. SNF
9. SNSF
10. FNS
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1. PP00P2 138932
2. 407540 167186 NFP 75 Big Data
H2020 European Research Council 10.13039/100010663
Region: Europe
gov (National government)
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1. H2020 Excellent Science - European Research Council
2. European Research Council
3. H2020 Wissenschaftsexzellenz - Für das Einzelziel 'Europäischer Forschungsrat (ERC)'
4. H2020 Ciencia Excelente - Consejo Europeo de Investigación (CEI)
5. H2020 Excellence Scientifique - Conseil européen de la recherche (CER)
6. H2020 Eccellenza Scientifica - Consiglio europeo della ricerca (CER)
7. H2020 Doskonała Baza Naukowa - Europejska Rada ds. Badań Naukowych (ERBN)
8. EXCELLENT SCIENCE - European Research Council
9. WISSENSCHAFTSEXZELLENZ - Für das Einzelziel 'Europäischer Forschungsrat
10. CIENCIA EXCELENTE - Consejo Europeo de Investigación
11. EXCELLENCE SCIENTIFIQUE - Conseil européen de la recherche
12. ECCELLENZA SCIENTIFICA - Consiglio europeo della ricerca
13. DOSKONAŁA BAZA NAUKOWA - Europejska Rada ds. Badań Naukowych
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15. CEI
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BibTeX

@article{Bereau_2018, title={Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning}, volume={148}, ISSN={1089-7690}, url={http://dx.doi.org/10.1063/1.5009502}, DOI={10.1063/1.5009502}, number={24}, journal={The Journal of Chemical Physics}, publisher={AIP Publishing}, author={Bereau, Tristan and DiStasio, Robert A. and Tkatchenko, Alexandre and von Lilienfeld, O. Anatole}, year={2018}, month=mar }

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