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Journal of the American Chemical Society
|
December 15, 2010
Dynamics of a myoglobin mutant enzyme: 2D IR vibrational echo experiments and simulations
Sayan Bagchi, Benjamin T Nebgen, Roger F Loring, et al.
Nature Communications
|
August 12, 2021
Teaching a neural network to attach and detach electrons from molecules
Roman Zubatyuk, Justin S Smith, Benjamin T Nebgen, et al.
Nature Communications
|
July 3, 2019
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, et al.
The Journal of Physical Chemistry Letters
|
July 25, 2018
Discovering a Transferable Charge Assignment Model Using Machine Learning
Andrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, et al.
Journal of Chemical Information and Modeling
|
January 28, 2025
Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation
Luan G F Dos Santos, Benjamin T Nebgen, Alice E A Allen, et al.
Journal of Chemical Theory and Computation
|
March 14, 2025
Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential
Cheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, et al.
Faraday Discussions
|
September 29, 2017
Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquids
Benjamin T Nebgen, Harsha D Magurudeniya, Kevin W C Kwock, et al.
Nature Chemistry
|
March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
Shuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Journal of Chemical Theory and Computation
|
June 7, 2018
Photoexcited Nonadiabatic Dynamics of Solvated Push-Pull π-Conjugated Oligomers with the NEXMD Software
Andrew E Sifain, Josiah A Bjorgaard, Tammie R Nelson, et al.
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of 1
Search research articles
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Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 1
Journal of the American Chemical Society
|
December 15, 2010
Dynamics of a myoglobin mutant enzyme: 2D IR vibrational echo experiments and simulations
Sayan Bagchi, Benjamin T Nebgen, Roger F Loring, et al.
Nature Communications
|
August 12, 2021
Teaching a neural network to attach and detach electrons from molecules
Roman Zubatyuk, Justin S Smith, Benjamin T Nebgen, et al.
Nature Communications
|
July 3, 2019
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, et al.
The Journal of Physical Chemistry Letters
|
July 25, 2018
Discovering a Transferable Charge Assignment Model Using Machine Learning
Andrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, et al.
Journal of Chemical Information and Modeling
|
January 28, 2025
Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation
Luan G F Dos Santos, Benjamin T Nebgen, Alice E A Allen, et al.
Journal of Chemical Theory and Computation
|
March 14, 2025
Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential
Cheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, et al.
Faraday Discussions
|
September 29, 2017
Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquids
Benjamin T Nebgen, Harsha D Magurudeniya, Kevin W C Kwock, et al.
Nature Chemistry
|
March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
Shuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Journal of Chemical Theory and Computation
|
June 7, 2018
Photoexcited Nonadiabatic Dynamics of Solvated Push-Pull π-Conjugated Oligomers with the NEXMD Software
Andrew E Sifain, Josiah A Bjorgaard, Tammie R Nelson, et al.
Page
of 1