Chen Qu , Paul L. Houston , Qi Yu , Priyanka Pandey , Riccardo Conte , Apurba Nandi , Joel M. Bowman
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Machine learning software to learn negligible elements of the Hamiltonian matrix
As a follow-up to our recent Communication in the Journal of Chemical Physics [J. Chem. Phys. 159 071101 (2023)], we report and make available the Jupyter Notebook software here. This software performs binary machine learning classification (MLC) with the goal of learning negligible Hamiltonian matrix elements for vibrational dynamics. We illustrate its usefulness for a Hamiltonian matrix for H2O by using three MLC algorithms: Random Forest, Support Vector Machine, and Multi-layer Perceptron.
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