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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. Here 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 HartreeFock, 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:
 [1];  [2];  [1];  [3]
+ Show Author Affiliations
  1. University of Basel (Switzerland)
  2. Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr (Germany); Friedrich-Alexander University Erlangen-Nuremberg, Bamberg (Germany)
  3. University of Basel (Switzerland); Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Swiss National Science Foundation (SNSF)
OSTI Identifier:
1392925
Grant/Contract Number:  
AC02-06CH11357; PP00P2_138932
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 11; Journal Issue: 5; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Ramakrishnan, Raghunathan, Dral, Pavlo O., Rupp, Matthias, and von Lilienfeld, O. Anatole. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. United States: N. p., 2015. Web. doi:10.1021/acs.jctc.5b00099.
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Ramakrishnan, Raghunathan, Dral, Pavlo O., Rupp, Matthias, & von Lilienfeld, O. Anatole. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. United States. https://doi.org/10.1021/acs.jctc.5b00099
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Ramakrishnan, Raghunathan, Dral, Pavlo O., Rupp, Matthias, and von Lilienfeld, O. Anatole. Fri . "Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach". United States. https://doi.org/10.1021/acs.jctc.5b00099. https://www.osti.gov/servlets/purl/1392925.
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@article{osti_1392925,
title = {Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach},
author = {Ramakrishnan, Raghunathan and Dral, Pavlo O. and Rupp, Matthias and von Lilienfeld, O. Anatole},
abstractNote = {Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. Here 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 HartreeFock, 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.},
doi = {10.1021/acs.jctc.5b00099},
journal = {Journal of Chemical Theory and Computation},
number = 5,
volume = 11,
place = {United States},
year = {Fri Apr 10 00:00:00 EDT 2015},
month = {Fri Apr 10 00:00:00 EDT 2015}
}
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Publisher's Version of Record
https://doi.org/10.1021/acs.jctc.5b00099
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