title: Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.
abstract: We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurateab initioatomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.
authors.affiliation: Max-Planck-Institut für Kohlenforschung , Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, 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 ; Argonne Leadership Computing Facility, Argonne National Laboratory , Argonne, Illinois 60439, United States.
authors.forename: O Anatole
authors.initials: OA
authors.lastname: von Lilienfeld
authors.affiliation: Max-Planck-Institut für Kohlenforschung , Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany.
authors.forename: Walter
authors.initials: W
authors.lastname: Thiel