Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations
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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1578167
- Journal Information:
- MRS Communications, Vol. 9, Issue 3; ISSN 2159-6859
- Publisher:
- Materials Research Society - Cambridge University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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journal | October 2019 |
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