Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Open accessAbstract
We combine the approximate density-functional tight-binding (DFTB) method with unsupervised machine learning. This allows us to improve transferability and accuracy, make use of large quantum chemical data sets for the parametrization, and efficiently automatize the parametrization process of DFTB. For this purpose, generalized pair-potentials are introduced, where the chemical environment is included during the learning process, leading to more specific effective two-body potentials. We train on energies and forces of equilibrium and nonequilibrium structures of 2100 molecules, and test on ∼130 000 organic molecules containing O, N, C, H, and F atoms. Atomization energies of the reference method can be reproduced within an error of ∼2.6 kcal/mol, indicating drastic improvement over standard DFTB.
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Clustering can be used to select the most relevant reference configurations from a database, or to partition it into regions on which specialized models can be trained.
“Clustering can be used to select the most relevant reference configurations from a database 109 , or to partition it into regions on which specialized models can be trained. 110”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [110]
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Schwade, M., Schilcher, M. J., Baecker, C. R., Grumet, M., & Egger, D. A. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0197986 · Cited by 2 · see citing works
Introductory Roadmap to Current Reactive Force-Field Methodologies
Bresnahan, C. G., Jenness, G. R., Kumar, R., & Shukla, M. K. (2021) · Springer eBooks · doi:10.1007/978-3-030-83244-5_1 · Cited by 2 · see citing works
Non-adiabatic molecular dynamics of PAH-related complexes
Posenitskiy, E. (2020) · theses.fr (ABES) · doi:10.70675/bfb35103z00bbz4bf9z80fdze96b19b060bb · Cited by 2 · see citing works
Programmable repulsive potential for tight-binding from Chen-Möbius inversion theorem
Li, J., Tang, J.-K., Song, H., Seifert, G., & Zhang, D. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-023-2200-x
Self-consistent equations for nonempirical tight-binding theory
Mironenko, A. V., Leung, L., & Zhuang, J. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0276043
Optimizing Extended Tight‐Binding Methods for Metal‐Surface Interactions
Moradi, S., Dabbaghi, P., & Stein, C. J. (2025) · ChemPhysChem · doi:10.1002/cphc.202500463
- Authors
- Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- University of Basel (CH)
- Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
- Corresponding author
- Elstner, Marcus
- ORCID
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-03-26
- Volume
- 14
- Issue
- 5
- Pages
- 2341-2352
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2790700814
- Cited by
- 63
- References
- 49
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 3 · 2023: 10 · 2022: 9 · 2021: 6 · 2020: 9 · 2019: 11 · 2018: 5
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- green
- edoc.unibas.ch
- Licence
- other-oa
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion, other-oa)
- edoc (University of Basel)
- Funders
- Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (PP00P2_138932)
- Deutsche Forschungsgemeinschaft (SFB 1249)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- density functional tight binding, repulsive potential, unsupervised learning, potential parameterization, chemical environment, transferability, atomization energies, organic compounds, non-equilibrium structures
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