Unimolecular dissociation of C6H6-C6H5Cl, C6H6-C6H3Cl3, and C6H6-C6Cl6 complexes using machine learning approach

Basudha Deb; S R Ngamwal Anal; Himashree Mahanta; Yogita; Amit Kumar Paul

doi:10.1063/5.0139864

Unimolecular dissociation of C6H6-C6H5Cl, C6H6-C6H3Cl3, and C6H6-C6Cl6 complexes using machine learning approach

J Chem Phys. 2023 May 21;158(19):194104. doi: 10.1063/5.0139864.

Authors

Basudha Deb¹, S R Ngamwal Anal², Himashree Mahanta¹, Yogita², Amit Kumar Paul¹

Affiliations

¹ Department of Chemistry, National Institute of Technology Meghalaya, Shillong 793003, Meghalaya, India.
² Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong 793003, Meghalaya, India.

PMID: 37184007
DOI: 10.1063/5.0139864

Abstract

The application of Machine Learning (ML) algorithms in chemical sciences, particularly computational chemistry, is a vastly emerging area of modern research. While many applications of ML techniques have already been in place to use ML based potential energies in various dynamical simulation studies, specific applications are also being successfully tested. In this work, the ML algorithms are tested to calculate the unimolecular dissociation time of benzene-hexachlorobenzene, benzene-trichlorobenzene, and benzene-monochlorobenzene complexes. Three ML algorithms, namely, Decision-Tree-Regression (DTR), Multi-Layer Perceptron, and Support Vector Regression are considered. The algorithms are trained with simulated dissociation times as functions (attributes) of complexes' intramolecular and intermolecular vibrational energies. The simulation data are used for an excitation temperature of 1500 K. Considering that the converged result is obtained with 1500 trajectories, an ML algorithm trained with 700 simulation points provides the same dissociation rate constant within statistical uncertainty as obtained from the converged 1500 trajectory result. The DTR algorithm is also used to predict 1000 K simulation results using 1500 K simulation data.