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

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

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]

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

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

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

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