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 matr...

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Podrobná bibliografie
Vydáno v:	Artificial intelligence chemistry Ročník 1; číslo 2; s. 100025
Hlavní autoři:	Qu, Chen, Houston, Paul L., Yu, Qi, Pandey, Priyanka, Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier 01.12.2023
Témata:	Machine Learning Classification Vibrational Configuration Interaction
ISSN:	2949-7477, 2949-7477
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
ISSN:	2949-7477 2949-7477
DOI:	10.1016/j.aichem.2023.100025