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Issue 35, 2018
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Machine learning prediction of interaction energies in rigid water clusters

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Abstract

Classical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol−1 and 0.34 kcal mol−1, respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.

Graphical abstract: Machine learning prediction of interaction energies in rigid water clusters

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Supplementary files

Article information


Submitted
17 May 2018
Accepted
13 Aug 2018
First published
14 Aug 2018

Phys. Chem. Chem. Phys., 2018,20, 22987-22996
Article type
Paper

Machine learning prediction of interaction energies in rigid water clusters

S. Bose, D. Dhawan, S. Nandi, R. R. Sarkar and D. Ghosh, Phys. Chem. Chem. Phys., 2018, 20, 22987
DOI: 10.1039/C8CP03138J

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