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Andrea Grisafi

Showing results (1-10 of 13) with videos related to

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The Journal of Chemical Physics|November 30, 2019
Incorporating long-range physics in atomic-scale machine learningAndrea Grisafi, Michele Ceriotti
The Journal of Chemical Physics|July 10, 2024
Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densitiesAndrea Grisafi, Mathieu Salanne
Chemical Science|June 24, 2021
Multi-scale approach for the prediction of atomic scale propertiesAndrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Chimia|April 26, 2020
Learning (from) the Electron Density: Transferability, Conformational and Chemical DiversityAlberto Fabrizio, Ksenia Briling, Andrea Grisafi, et al.
Physical Review Letters|February 6, 2018
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic SystemsAndrea Grisafi, David M Wilkins, Gábor Csányi, et al.
Journal of Chemical Theory and Computation|October 20, 2021
Learning Electron Densities in the Condensed PhaseAlan M Lewis, Andrea Grisafi, Michele Ceriotti, et al.
Journal of Chemical Theory and Computation|December 1, 2022
Electronic-Structure Properties from Atom-Centered Predictions of the Electron DensityAndrea Grisafi, Alan M Lewis, Mariana Rossi, et al.
Chemical Science|February 15, 2020
Electron density learning of non-covalent systemsAlberto Fabrizio, Andrea Grisafi, Benjamin Meyer, et al.
The Journal of Physical Chemistry Letters|February 24, 2025
Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field ModelMariana Rossi, Kevin Rossi, Alan M Lewis, et al.
Chemical Reviews|July 26, 2021
Physics-Inspired Structural Representations for Molecules and MaterialsFelix Musil, Andrea Grisafi, Albert P Bartók, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
The Journal of Chemical Physics|November 30, 2019
Incorporating long-range physics in atomic-scale machine learningAndrea Grisafi, Michele Ceriotti
The Journal of Chemical Physics|July 10, 2024
Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densitiesAndrea Grisafi, Mathieu Salanne
Chemical Science|June 24, 2021
Multi-scale approach for the prediction of atomic scale propertiesAndrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Chimia|April 26, 2020
Learning (from) the Electron Density: Transferability, Conformational and Chemical DiversityAlberto Fabrizio, Ksenia Briling, Andrea Grisafi, et al.
Physical Review Letters|February 6, 2018
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic SystemsAndrea Grisafi, David M Wilkins, Gábor Csányi, et al.
Journal of Chemical Theory and Computation|October 20, 2021
Learning Electron Densities in the Condensed PhaseAlan M Lewis, Andrea Grisafi, Michele Ceriotti, et al.
Journal of Chemical Theory and Computation|December 1, 2022
Electronic-Structure Properties from Atom-Centered Predictions of the Electron DensityAndrea Grisafi, Alan M Lewis, Mariana Rossi, et al.
Chemical Science|February 15, 2020
Electron density learning of non-covalent systemsAlberto Fabrizio, Andrea Grisafi, Benjamin Meyer, et al.
The Journal of Physical Chemistry Letters|February 24, 2025
Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field ModelMariana Rossi, Kevin Rossi, Alan M Lewis, et al.
Chemical Reviews|July 26, 2021
Physics-Inspired Structural Representations for Molecules and MaterialsFelix Musil, Andrea Grisafi, Albert P Bartók, et al.
Pageof 2