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

Published online by Cambridge University Press:  27 August 2019

Logan Ward*
Affiliation:
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA Department of Computer Science, University of Chicago, Chicago, IL, USA
Ben Blaiszik
Affiliation:
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA Globus, University of Chicago, Chicago, IL, USA
Ian Foster
Affiliation:
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA Department of Computer Science, University of Chicago, Chicago, IL, USA Globus, University of Chicago, Chicago, IL, USA
Rajeev S. Assary
Affiliation:
Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, IL, USA Materials Science Division, Argonne National Laboratory, Lemont, IL, USA
Badri Narayanan
Affiliation:
Materials Science Division, Argonne National Laboratory, Lemont, IL, USA Department of Mechanical Engineering, University of Louisville, Louisville, KY, USA
Larry Curtiss
Affiliation:
Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, IL, USA Materials Science Division, Argonne National Laboratory, Lemont, IL, USA
*
Address all correspondence to Logan Ward at lward@anl.gov
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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. 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.

Type
Artificial Intelligence Research Letters
Copyright
Copyright © The Author(s) 2019 

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