Quantum-chemical insights from deep tensor neural networks

Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller () and Alexandre Tkatchenko ()
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Kristof T. Schütt: Machine Learning Group, Technische Universität Berlin
Farhad Arbabzadah: Machine Learning Group, Technische Universität Berlin
Stefan Chmiela: Machine Learning Group, Technische Universität Berlin
Klaus R. Müller: Machine Learning Group, Technische Universität Berlin
Alexandre Tkatchenko: Fritz-Haber-Institut der Max-Planck-Gesellschaft

Nature Communications, 2017, vol. 8, issue 1, 1-8

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/ncomms13890

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