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Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Raghunathan Ramakrishnan Pavlo O. Dral Matthias Rupp O. Anatole von Lilienfeld,
Project From atomistic exploration of chemical compound space towards bio-molecular design: Quantum mechanical rational compound design (QM-RCD)
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Original article (peer-reviewed)

Journal Journal of Chemical Theory and Computation
Page(s) 2087
Title of proceedings Journal of Chemical Theory and Computation
DOI 10.1021/acs.jctc.5b00099


Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree–Fock methods, at the computational cost of Hartree–Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.