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Mach. Learn. Sci. Technol.: A comparative study of different machine learning methods for dissipative quantum dynamics

2022-11-02

The comparative study is performed for a general two-state spin-boson model where the performance of the models was assessed by the mean absolute error (MAE) and computational times for training and prediction.

New book: Quantum Chemistry in the Age of Machine Learning

2022-10-04

Prof. Pavlo O. Dral's book “Quantum Chemistry in the Age of Machine Learning” was published by Elsevier on 16th September, 2022.

Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn]

2022-09-21

MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school.

J. Chem. Phys.: the classic but challenging covalent-ionic interaction in LiF

2022-09-09

J. Chem. Phys. 157, 084106 (2022); doi: 10.1063/5.0097614

J. Chem. Phys.: A general tight-binding based energy decomposition analysis scheme for intermolecular interactions in large molecules

2022-07-19

J. Chem. Phys. 157, 034104 (2022); https://doi.org/10.1063/5.0091781

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