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Kristof T Schütt

Showing results (1-10 of 12) with videos related to

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Chemical Science|September 27, 2021
Machine learning of solvent effects on molecular spectra and reactionsMichael Gastegger, Kristof T Schütt, Klaus-Robert Müller
The Journal of Chemical Physics|July 9, 2021
Perspective on integrating machine learning into computational chemistry and materials scienceJulia Westermayr, Michael Gastegger, Kristof T Schütt, et al.
Nature Communications|January 10, 2017
Quantum-chemical insights from deep tensor neural networksKristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, et al.
Chemical Science|August 30, 2024
PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance samplingJulian Cremer, Tuan Le, Frank Noé, et al.
Science Advances|May 17, 2017
Machine learning of accurate energy-conserving molecular force fieldsStefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, et al.
Science Advances|September 12, 2020
Autonomous robotic nanofabrication with reinforcement learningPhilipp Leinen, Malte Esders, Kristof T Schütt, et al.
The Journal of Chemical Physics|April 15, 2023
SchNetPack 2.0: A neural network toolbox for atomistic machine learningKristof T Schütt, Stefaan S P Hessmann, Niklas W A Gebauer, et al.
Physical Chemistry Chemical Physics : PCCP|September 26, 2023
Automatic identification of chemical moietiesJonas Lederer, Michael Gastegger, Kristof T Schütt, et al.
Nature Communications|December 15, 2021
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsOliver T Unke, Stefan Chmiela, Michael Gastegger, et al.
Nature Communications|February 22, 2022
Inverse design of 3d molecular structures with conditional generative neural networksNiklas W A Gebauer, Michael Gastegger, Stefaan S P Hessmann, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Chemical Science|September 27, 2021
Machine learning of solvent effects on molecular spectra and reactionsMichael Gastegger, Kristof T Schütt, Klaus-Robert Müller
The Journal of Chemical Physics|July 9, 2021
Perspective on integrating machine learning into computational chemistry and materials scienceJulia Westermayr, Michael Gastegger, Kristof T Schütt, et al.
Nature Communications|January 10, 2017
Quantum-chemical insights from deep tensor neural networksKristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, et al.
Chemical Science|August 30, 2024
PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance samplingJulian Cremer, Tuan Le, Frank Noé, et al.
Science Advances|May 17, 2017
Machine learning of accurate energy-conserving molecular force fieldsStefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, et al.
Science Advances|September 12, 2020
Autonomous robotic nanofabrication with reinforcement learningPhilipp Leinen, Malte Esders, Kristof T Schütt, et al.
The Journal of Chemical Physics|April 15, 2023
SchNetPack 2.0: A neural network toolbox for atomistic machine learningKristof T Schütt, Stefaan S P Hessmann, Niklas W A Gebauer, et al.
Physical Chemistry Chemical Physics : PCCP|September 26, 2023
Automatic identification of chemical moietiesJonas Lederer, Michael Gastegger, Kristof T Schütt, et al.
Nature Communications|December 15, 2021
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsOliver T Unke, Stefan Chmiela, Michael Gastegger, et al.
Nature Communications|February 22, 2022
Inverse design of 3d molecular structures with conditional generative neural networksNiklas W A Gebauer, Michael Gastegger, Stefaan S P Hessmann, et al.
Pageof 2