Gaussian Process Regression for Materials and Molecules (English)
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- New search for: Deringer, Volker L.
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- New search for: Bartók, Albert P.
- New search for: Bernstein, Noam
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- New search for: Wilkins, David M.
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- New search for: Ceriotti, Michele
- Further information on Ceriotti, Michele:
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- New search for: Csányi, Gábor
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- New search for: Deringer, Volker L.
- Further information on Deringer, Volker L.:
- https://orcid.org/0000-0001-6873-0278
- New search for: Bartók, Albert P.
- New search for: Bernstein, Noam
- Further information on Bernstein, Noam:
- https://orcid.org/0000-0002-6532-1337
- New search for: Wilkins, David M.
- Further information on Wilkins, David M.:
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- New search for: Ceriotti, Michele
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In:
Chemical Reviews
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121
, 16
;
10073-10141
;
2021
- Article (Journal) / Electronic Resource
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Title:Gaussian Process Regression for Materials and Molecules
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Additional title:Chem. Rev.
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Contributors:Deringer, Volker L. ( author ) / Bartók, Albert P. ( author ) / Bernstein, Noam ( author ) / Wilkins, David M. ( author ) / Ceriotti, Michele ( author ) / Csányi, Gábor ( author )
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Published in:Chemical Reviews ; 121, 16 ; 10073-10141
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Publisher:
- New search for: American Chemical Society
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Publication date:2021-08-25
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ISSN:
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Coden:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Licence:
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Source:
Table of contents – Volume 121, Issue 16
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 9719
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Introduction: Machine Learning at the Atomic ScaleCeriotti, Michele / Clementi, Cecilia / Anatole von Lilienfeld, O. et al. | 2021
- 9722
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Unsupervised Learning Methods for Molecular Simulation DataGlielmo, Aldo / Husic, Brooke E. / Rodriguez, Alex / Clementi, Cecilia / Noé, Frank / Laio, Alessandro et al. | 2021
- 9759
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Physics-Inspired Structural Representations for Molecules and MaterialsMusil, Felix / Grisafi, Andrea / Bartók, Albert P. / Ortner, Christoph / Csányi, Gábor / Ceriotti, Michele et al. | 2021
- 9816
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical SystemsKeith, John A. / Vassilev-Galindo, Valentin / Cheng, Bingqing / Chmiela, Stefan / Gastegger, Michael / Müller, Klaus-Robert / Tkatchenko, Alexandre et al. | 2021
- 9873
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Machine Learning for Electronically Excited States of MoleculesWestermayr, Julia / Marquetand, Philipp et al. | 2021
- 9927
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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine LearningNandy, Aditya / Duan, Chenru / Taylor, Michael G. / Liu, Fang / Steeves, Adam H. / Kulik, Heather J. et al. | 2021
- 10001
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Ab Initio Machine Learning in Chemical Compound SpaceHuang, Bing / von Lilienfeld, O. Anatole et al. | 2021
- 10037
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Four Generations of High-Dimensional Neural Network PotentialsBehler, Jörg et al. | 2021
- 10073
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Gaussian Process Regression for Materials and MoleculesDeringer, Volker L. / Bartók, Albert P. / Bernstein, Noam / Wilkins, David M. / Ceriotti, Michele / Csányi, Gábor et al. | 2021
- 10142
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Machine Learning Force FieldsUnke, Oliver T. / Chmiela, Stefan / Sauceda, Huziel E. / Gastegger, Michael / Poltavsky, Igor / Schütt, Kristof T. / Tkatchenko, Alexandre / Müller, Klaus-Robert et al. | 2021
- 10187
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Neural Network Potential Energy Surfaces for Small Molecules and ReactionsManzhos, Sergei / Carrington, Tucker et al. | 2021
- 10218
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Machine Learning for Chemical ReactionsMeuwly, Markus et al. | 2021