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.

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

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

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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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Introduciton to XEDA in XACS

XEDA employs LMO-EDA, GKS-EDA and their extensions to explore the physical origin of intermolecular interactions.

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J. Chem. Phys: λ-DFVB(U): A hybrid density functional valence bond method based on unpaired electron density

https://doi.org/10.1063/5.0091592

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