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.
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Acknowledgments
This research was supported in part by the Exascale Computing Project (17-SC-20-SC) of the U.S. Department of Energy (DOE), by DOE’s Advanced Scientific Research Office (ASCR) under contract DE-AC02-06CH11357, and by the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. This work used resources from the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation Grant No. ACI-1548562:[38] specifically, Jetstream at the Texas Advanced Computing Center through allocation CIE170012;[39] the University of Chicago Research Computing Center; and the Argonne Leadership Computing Facility. This material is based upon work supported by Laboratory Directed Research and Development (LDRD) fund-ing from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This work was per-formed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD). This work was also supported by the National Science Foundation as part of the Midwest Big Data Hub under NSF Award Number: 1636950 “BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate.”
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Ward, L., Blaiszik, B., Foster, I. et al. Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations. MRS Communications 9, 891–899 (2019). https://doi.org/10.1557/mrc.2019.107
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DOI: https://doi.org/10.1557/mrc.2019.107