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Igor Poltavsky

Showing results (11-20 of 19) with videos related to

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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.
Nature Communications|May 21, 2024
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptorsMiguel Gallegos, Valentin Vassilev-Galindo, Igor Poltavsky, et al.
The Journal of Chemical Physics|March 24, 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forcesHuziel E Sauceda, Stefan Chmiela, Igor Poltavsky, et al.
Science Advances|May 17, 2017
Machine learning of accurate energy-conserving molecular force fieldsStefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, et al.
The Journal of Chemical Physics|January 3, 2019
Stability of functionalized platform molecules on Au(111)Torben Jasper-Tönnies, Igor Poltavsky, Sandra Ulrich, et al.
Physical Review Letters|April 23, 2016
Thermal and Electronic Fluctuations of Flexible Adsorbed Molecules: Azobenzene on Ag(111)Reinhard J Maurer, Wei Liu, Igor Poltavsky, et al.
Chemical Reviews|March 11, 2021
Machine Learning Force FieldsOliver T Unke, Stefan Chmiela, Huziel E Sauceda, 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 2

Showing results (11-20 of 19) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 19 results.
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.
Nature Communications|May 21, 2024
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptorsMiguel Gallegos, Valentin Vassilev-Galindo, Igor Poltavsky, et al.
The Journal of Chemical Physics|March 24, 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forcesHuziel E Sauceda, Stefan Chmiela, Igor Poltavsky, et al.
Science Advances|May 17, 2017
Machine learning of accurate energy-conserving molecular force fieldsStefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, et al.
The Journal of Chemical Physics|January 3, 2019
Stability of functionalized platform molecules on Au(111)Torben Jasper-Tönnies, Igor Poltavsky, Sandra Ulrich, et al.
Physical Review Letters|April 23, 2016
Thermal and Electronic Fluctuations of Flexible Adsorbed Molecules: Azobenzene on Ag(111)Reinhard J Maurer, Wei Liu, Igor Poltavsky, et al.
Chemical Reviews|March 11, 2021
Machine Learning Force FieldsOliver T Unke, Stefan Chmiela, Huziel E Sauceda, 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.
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