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Adil Kabylda

Showing results (1-10 of 7) with videos related to

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Nature Communications|July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Nature Communications|June 15, 2023
Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Journal of Chemical Theory and Computation|January 10, 2025
Analyzing Atomic Interactions in Molecules as Learned by Neural NetworksMalte Esders, Thomas Schnake, Jonas Lederer, et al.
Science Advances|January 11, 2023
Accurate global machine learning force fields for molecules with hundreds of atomsStefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, et al.
Journal of the American Chemical Society|August 31, 2025
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force FieldsAdil Kabylda, J Thorben Frank, Sergio Suárez-Dou, et al.
Chemical Science|February 6, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Chemical Science|February 12, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Nature Communications|July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Nature Communications|June 15, 2023
Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
Journal of Chemical Theory and Computation|January 10, 2025
Analyzing Atomic Interactions in Molecules as Learned by Neural NetworksMalte Esders, Thomas Schnake, Jonas Lederer, et al.
Science Advances|January 11, 2023
Accurate global machine learning force fields for molecules with hundreds of atomsStefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, et al.
Journal of the American Chemical Society|August 31, 2025
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force FieldsAdil Kabylda, J Thorben Frank, Sergio Suárez-Dou, et al.
Chemical Science|February 6, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Chemical Science|February 12, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, et al.
Pageof 1