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Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Pavlo O. Dral O. Anatole von Lilienfeld Walter Thiel,
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) 2120
Title of proceedings Journal of Chemical Theory and Computation
DOI 10.1021/acs.jctc.5b00141

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 accurate ab initio atomization 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.
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