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Theoretical and Computational Chemistry

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Speeding up quantum dissipative dynamics of open systems with kernel methods

Arif Ullah, Pavlo O. Dral

Version 2 posted 27 September 2021

463

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Theoretical and Computational Chemistry

Working Paper

A comparative study of different machine learning methods for dissipative quantum dynamics

Luis E. Herrera Rodr ́ıguez, Arif Ullah, Kennet J. Rueda Espinosa, Pavlo O. Dral, Alexei A. Kananenka

Version 2 posted 13 September 2022

301

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Theoretical and Computational Chemistry

Working Paper

MLQD: A package for machine learning-based quantum dissipative dynamics

Arif Ullah, Pavlo O. Dral

Version 3 posted 20 September 2023

353

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Theoretical and Computational Chemistry

Working Paper

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Onur Çaylak, Björn Baumeier

Version 1 posted 24 May 2021

315

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Physical Chemistry

Working Paper

Fast and accurate excited states predictions: Machine learning and diabatization

Štěpán Sršeň, O. Anatole von Lilienfeld, Petr Slavicek

Version 1 posted 26 May 2023

473

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Theoretical and Computational Chemistry

Working Paper

Size-Extensive Molecular Machine Learning with Global Descriptors

Hyunwook Jung, Sina Stocker, Christian Kunkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf

Version 1 posted 22 October 2019

849

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1

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Theoretical and Computational Chemistry

Working Paper

Explicit learning of derivatives with the KREG and pKREG models on the example of accurate representation of molecular potential energy surfaces

Yi-Fan Hou, Fuchun Ge, Pavlo O. Dral

Version 1 posted 20 October 2022

440

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1

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Theoretical and Computational Chemistry

Working Paper

Machine Learning for Absorption Cross Sections

Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral

Version 2 posted 22 July 2020

733

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Theoretical and Computational Chemistry

Working Paper

27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction

Daniel Willimetz, Andreas Erlebach, Christopher J. Heard, Lukáš Grajciar

Version 1 posted 24 September 2024

131

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Refine Search

9 results in Kernel ridge regression in Keywords: Kernel ridge regression

Speeding up quantum dissipative dynamics of open systems with kernel methods

A comparative study of different machine learning methods for dissipative quantum dynamics

MLQD: A package for machine learning-based quantum dissipative dynamics

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Fast and accurate excited states predictions: Machine learning and diabatization

Size-Extensive Molecular Machine Learning with Global Descriptors

Explicit learning of derivatives with the KREG and pKREG models on the example of accurate representation of molecular potential energy surfaces

Machine Learning for Absorption Cross Sections

27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction

9 results in Keywords: Kernel ridge regression