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The Journal of Physical Chemistry. A
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April 14, 2025
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
Yi-Fan Hou, Cheng Wang, Pavlo O Dral
Journal of Chemical Theory and Computation
|
July 7, 2015
Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations
Pavlo O Dral, O Anatole von Lilienfeld, Walter Thiel
Scientific Data
|
February 15, 2023
WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets
Max Pinheiro, Shuang Zhang, Pavlo O Dral, et al.
The Journal of Chemical Physics
|
June 4, 2020
Hierarchical machine learning of potential energy surfaces
Pavlo O Dral, Alec Owens, Alexey Dral, et al.
Chemical Science
|
February 2, 2026
Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients
Mikołaj Martyka, Joanna Jankowska, Hans Lischka, et al.
Journal of Computational Chemistry
|
December 15, 2018
Big data analysis of ab Initio molecular integrals in the neglect of diatomic differential overlap approximation
Xin Wu, Pavlo O Dral, Axel Koslowski, et al.
Physical Chemistry Chemical Physics : PCCP
|
July 13, 2023
Themed collection on Insightful Machine Learning for Physical Chemistry
Aurora E Clark, Pavlo O Dral, Isaac Tamblyn, et al.
The Journal of Chemical Physics
|
July 3, 2017
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
Pavlo O Dral, Alec Owens, Sergei N Yurchenko, et al.
Nature Communications
|
April 7, 2026
OMNI-P2x universal neural network potential for excited-state simulations
Mikołaj Martyka, Xin-Yu Tong, Joanna Jankowska, et al.
Physical Chemistry Chemical Physics : PCCP
|
August 24, 2023
Energy-conserving molecular dynamics is not energy conserving
Lina Zhang, Yi-Fan Hou, Fuchun Ge, et al.
Page
of 9
Search research articles
Search
Showing results (21-30 of 82) with videos related to
Sort By:
Page
of 9
The Journal of Physical Chemistry. A
|
April 14, 2025
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
Yi-Fan Hou, Cheng Wang, Pavlo O Dral
Journal of Chemical Theory and Computation
|
July 7, 2015
Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations
Pavlo O Dral, O Anatole von Lilienfeld, Walter Thiel
Scientific Data
|
February 15, 2023
WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets
Max Pinheiro, Shuang Zhang, Pavlo O Dral, et al.
The Journal of Chemical Physics
|
June 4, 2020
Hierarchical machine learning of potential energy surfaces
Pavlo O Dral, Alec Owens, Alexey Dral, et al.
Chemical Science
|
February 2, 2026
Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients
Mikołaj Martyka, Joanna Jankowska, Hans Lischka, et al.
Journal of Computational Chemistry
|
December 15, 2018
Big data analysis of ab Initio molecular integrals in the neglect of diatomic differential overlap approximation
Xin Wu, Pavlo O Dral, Axel Koslowski, et al.
Physical Chemistry Chemical Physics : PCCP
|
July 13, 2023
Themed collection on Insightful Machine Learning for Physical Chemistry
Aurora E Clark, Pavlo O Dral, Isaac Tamblyn, et al.
The Journal of Chemical Physics
|
July 3, 2017
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
Pavlo O Dral, Alec Owens, Sergei N Yurchenko, et al.
Nature Communications
|
April 7, 2026
OMNI-P2x universal neural network potential for excited-state simulations
Mikołaj Martyka, Xin-Yu Tong, Joanna Jankowska, et al.
Physical Chemistry Chemical Physics : PCCP
|
August 24, 2023
Energy-conserving molecular dynamics is not energy conserving
Lina Zhang, Yi-Fan Hou, Fuchun Ge, et al.
Page
of 9