Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach

Moharreri, Ehsan; Pardakhti, Maryam; Srivastava, Ranjan; Suib, Steven L.

doi:10.1021/acscombsci.9b00028

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Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach

Moharreri, Ehsan·Pardakhti, Maryam·Srivastava, Ranjan·Suib, Steven L.

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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. In this paper, 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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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY computational chemistry computational material prediction deep learning machine learning molecular informatics

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Moharreri, Ehsan, Pardakhti, Maryam, Srivastava, Ranjan, Suib, Steven L.. 2019-08-07. Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach. https://doi.org/10.1021/acscombsci.9b00028

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