Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 14, Issue 5, Pages 2341-2352Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.7b00933
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Funding
- DFG-SFB [1249]
- DFG [INST 40/467-1 FUGG]
- Swiss National Science foundation [PP00P2_138932]
- NCCR MARVEL - Swiss National Science Foundation
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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 similar to 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 similar to 2.6 kcal/mol, indicating drastic improvement over standard DFTB.
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