Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach
- Ehsan MoharreriEhsan MoharreriInstitute of Material Science, University of Connecticut, Storrs, Connecticut 06269, United StatesMore by Ehsan Moharreri
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- Maryam PardakhtiMaryam PardakhtiDepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United StatesMore by Maryam Pardakhti
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- Ranjan Srivastava*Ranjan Srivastava*E-mail: [email protected] (R.S.).Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United StatesMore by Ranjan Srivastava
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- Steven L. Suib*Steven L. Suib*E-mail: [email protected] (S.L.S.).Institute of Material Science, University of Connecticut, Storrs, Connecticut 06269, United StatesDepartment of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United StatesMore by Steven L. Suib
Abstract
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. Here, we introduce a multistep method based on machine learning algorithms to estimate total energy on the basis of spatial coordinates and charges for various chemical structures, including organic molecules, inorganic molecules, and ions. This method quickly calculates total energy with 0.76 au in root-mean-square error (RMSE) and 1.5% in mean absolute percent error (MAPE) when tested on a database of optimized and unoptimized structures. Using similar molecular representations, experimental thermochemical properties were estimated, with MAPE as low as 6% and RMSE of 8 cal/mol·K for heat capacity in a 10-fold cross-validation.
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