Search research articles
Contact Us
Filters
Showing results (61-70 of 93) with videos related to
Page
of 10
Sort By:
Journal of Chemical Information and Modeling
|
May 27, 2024
Editorial: Machine Learning in Materials Science
Kenneth M Merz, Yee Siew Choong, Zoe Cournia, et al.
Scientific Data
|
March 20, 2023
Comprehensive exploration of graphically defined reaction spaces
Qiyuan Zhao, Sai Mahit Vaddadi, Michael Woulfe, et al.
Scientific Data
|
May 3, 2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, et al.
Chemical Science
|
May 26, 2023
Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, et al.
Journal of the American Chemical Society
|
July 25, 2024
Discovery of Crystallizable Organic Semiconductors with Machine Learning
Holly M Johnson, Filipp Gusev, Jordan T Dull, et al.
Chemical Science
|
June 14, 2024
<i>In silico</i> screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
Evgeny Gutkin, Filipp Gusev, Francesco Gentile, et al.
Journal of the American Chemical Society
|
October 12, 2021
Machine-Learning-Guided Discovery of <sup>19</sup>F MRI Agents Enabled by Automated Copolymer Synthesis
Marcus Reis, Filipp Gusev, Nicholas G Taylor, et al.
The Journal of Chemical Physics
|
July 9, 2021
Machine learned Hückel theory: Interfacing physics and deep neural networks
Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, 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.
Journal of Chemical Theory and Computation
|
August 2, 2018
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
Benjamin Nebgen, Nicholas Lubbers, Justin S Smith, et al.
Page
of 10
Search research articles
Search
Showing results (61-70 of 93) with videos related to
Sort By:
Page
of 10
Journal of Chemical Information and Modeling
|
May 27, 2024
Editorial: Machine Learning in Materials Science
Kenneth M Merz, Yee Siew Choong, Zoe Cournia, et al.
Scientific Data
|
March 20, 2023
Comprehensive exploration of graphically defined reaction spaces
Qiyuan Zhao, Sai Mahit Vaddadi, Michael Woulfe, et al.
Scientific Data
|
May 3, 2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, et al.
Chemical Science
|
May 26, 2023
Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, et al.
Journal of the American Chemical Society
|
July 25, 2024
Discovery of Crystallizable Organic Semiconductors with Machine Learning
Holly M Johnson, Filipp Gusev, Jordan T Dull, et al.
Chemical Science
|
June 14, 2024
<i>In silico</i> screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
Evgeny Gutkin, Filipp Gusev, Francesco Gentile, et al.
Journal of the American Chemical Society
|
October 12, 2021
Machine-Learning-Guided Discovery of <sup>19</sup>F MRI Agents Enabled by Automated Copolymer Synthesis
Marcus Reis, Filipp Gusev, Nicholas G Taylor, et al.
The Journal of Chemical Physics
|
July 9, 2021
Machine learned Hückel theory: Interfacing physics and deep neural networks
Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, 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.
Journal of Chemical Theory and Computation
|
August 2, 2018
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
Benjamin Nebgen, Nicholas Lubbers, Justin S Smith, et al.
Page
of 10