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Title: Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

Abstract

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. As a result, our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [2]
  1. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Louisville, Louisville, KY (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1578167
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 9; Journal Issue: 3; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, and Curtiss, Larry. Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations. United States: N. p., 2019. Web. doi:10.1557/mrc.2019.107.
Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, & Curtiss, Larry. Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations. United States. doi:10.1557/mrc.2019.107.
Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, and Curtiss, Larry. Tue . "Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations". United States. doi:10.1557/mrc.2019.107.
@article{osti_1578167,
title = {Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations},
author = {Ward, Logan and Blaiszik, Ben and Foster, Ian and Assary, Rajeev S. and Narayanan, Badri and Curtiss, Larry},
abstractNote = {Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. As a result, our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.},
doi = {10.1557/mrc.2019.107},
journal = {MRS Communications},
number = 3,
volume = 9,
place = {United States},
year = {2019},
month = {8}
}

Journal Article:
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