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  Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Dral, P. O., von Lilienfeld, O. A., & Thiel, W. (2015). Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations. Journal of Chemical Theory and Computation, 11(5), 2120-2125. doi:10.1021/acs.jctc.5b00141.

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Dral, Pavlo O.1, Author              
von Lilienfeld, O. Anatole 2, 3, Author
Thiel, Walter1, Author              
Affiliations:
1Research Department Thiel, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445590              
2Institute 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, ou_persistent22              
3Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, United States, ou_persistent22              

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 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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Language(s): eng - English
 Dates: 2015-02-122015-04-022015-05-12
 Publication Status: Published in print
 Pages: 6
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.jctc.5b00141
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Title: Journal of Chemical Theory and Computation
  Other : J. Chem. Theory Comput.
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 11 (5) Sequence Number: - Start / End Page: 2120 - 2125 Identifier: ISSN: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832