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Kipton Barros

Showing results (11-20 of 48) with videos related to

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The Journal of Chemical Physics|July 2, 2018
Hierarchical modeling of molecular energies using a deep neural networkNicholas Lubbers, Justin S Smith, Kipton Barros
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|November 13, 2007
Approaching equilibrium and the distribution of clustersHui Wang, Kipton Barros, Harvey Gould, et al.
Physical Review. E|May 20, 2022
Machine learning of consistent thermodynamic models using automatic differentiationDavid Rosenberger, Kipton Barros, Timothy C Germann, et al.
The Journal of Chemical Physics|September 16, 2020
Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluidGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.
The Journal of Physical Chemistry Letters|May 7, 2020
<i>Ex Machina</i> Determination of Structural Correlation FunctionsGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.
Proceedings of the National Academy of Sciences of the United States of America|July 1, 2022
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanicsGuoqing Zhou, Nicholas Lubbers, Kipton Barros, et al.
Journal of Chemical Theory and Computation|January 3, 2024
Machine Learning Framework for Modeling Exciton Polaritons in Molecular MaterialsXinyang Li, Nicholas Lubbers, Sergei Tretiak, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|May 21, 2010
Direct observation of stringlike collective motion in a two-dimensional driven granular fluidChristian R Berardi, Kipton Barros, Jack F Douglas, et al.
Scientific Reports|April 27, 2016
Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learningBertrand Rouet-Leduc, Kipton Barros, Turab Lookman, et al.
Nature Communications|June 19, 2023
CP<sup>2</sup> skyrmions and skyrmion crystals in realistic quantum magnetsHao Zhang, Zhentao Wang, David Dahlbom, et al.
Pageof 5

Showing results (11-20 of 48) with videos related to

Sort By:
Pageof 5
The Journal of Chemical Physics|July 2, 2018
Hierarchical modeling of molecular energies using a deep neural networkNicholas Lubbers, Justin S Smith, Kipton Barros
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|November 13, 2007
Approaching equilibrium and the distribution of clustersHui Wang, Kipton Barros, Harvey Gould, et al.
Physical Review. E|May 20, 2022
Machine learning of consistent thermodynamic models using automatic differentiationDavid Rosenberger, Kipton Barros, Timothy C Germann, et al.
The Journal of Chemical Physics|September 16, 2020
Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluidGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.
The Journal of Physical Chemistry Letters|May 7, 2020
<i>Ex Machina</i> Determination of Structural Correlation FunctionsGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.
Proceedings of the National Academy of Sciences of the United States of America|July 1, 2022
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanicsGuoqing Zhou, Nicholas Lubbers, Kipton Barros, et al.
Journal of Chemical Theory and Computation|January 3, 2024
Machine Learning Framework for Modeling Exciton Polaritons in Molecular MaterialsXinyang Li, Nicholas Lubbers, Sergei Tretiak, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|May 21, 2010
Direct observation of stringlike collective motion in a two-dimensional driven granular fluidChristian R Berardi, Kipton Barros, Jack F Douglas, et al.
Scientific Reports|April 27, 2016
Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learningBertrand Rouet-Leduc, Kipton Barros, Turab Lookman, et al.
Nature Communications|June 19, 2023
CP<sup>2</sup> skyrmions and skyrmion crystals in realistic quantum magnetsHao Zhang, Zhentao Wang, David Dahlbom, et al.
Pageof 5