Quantum-chemical insights from deep tensor neural networks
Open accessAbstract
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol −1 ) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.
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- Authors
- Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A.
- Affiliations
- Technische Universität Berlin (DE)
- Korea University (KR)
- University of Luxembourg (LU)
- Corresponding author
- Müller, Klaus R.
- Tkatchenko, Alexandre
- Venue
- Nature Communications
- Published
- 2017-01-09
- Volume
- 8
- Issue
- 1
- Pages
- 13890-13890
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5228054
- OpenAlex
- W2527189750
- Cited by
- 1,445
- References
- 55
- Citations by year
- 2026: 89 · 2025: 155 · 2024: 167 · 2023: 170 · 2022: 147 · 2021: 182 · 2020: 213 · 2019: 164 · 2018: 126 · 2017: 29 · 2012: 1
- FWCI
- 61.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- National Research Foundation (2012-005741)
- Deutsche Forschungsgemeinschaft (MU 987/20-1, MU 987/20)
- Bundesministerium für Bildung und Forschung (01 IS 14013 A)
- Banting and Best Diabetes Centre, University of Toronto (01IS14013A)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- many-body Hamiltonian, molecular energy prediction, local chemical potential, atomic energy, electronic structure, quantum many-body systems
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