title: Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
abstract: 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.
authors.affiliation: Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel , Klingelbergstraße 80, CH-4056 Basel, Switzerland.
authors.forename: Raghunathan
authors.initials: R
authors.lastname: Ramakrishnan
authors.affiliation: Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany.|Computer-Chemie-Centrum and Interdisciplinary Center for Molecular Materials, Department Chemie und Pharmazie, Friedrich-Alexander-Universität Erlangen-Nürnberg , Nägelsbachstraße 25, 91052 Erlangen, Germany.
authors.forename: Pavlo O
authors.initials: PO
authors.lastname: Dral
authors.affiliation: Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel , Klingelbergstraße 80, CH-4056 Basel, Switzerland.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.affiliation: Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel , Klingelbergstraße 80, CH-4056 Basel, Switzerland.|Argonne Leadership Computing Facility, Argonne National Laboratory , 9700 S. Cass Avenue, Lemont, Illinois 60439, United States.
authors.forename: O Anatole
authors.initials: OA
authors.lastname: von Lilienfeld
mesh_terms: D004583:Electrons; D007536:Isomerism; D007659:Ketones / Q000737:chemistry; D000069550:Machine Learning; D011789:Quantum Theory; D013816:Thermodynamics
chemical_list: C583588:2-acetylcyclopentanone; D007659:Ketones