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Kipton Barros

Showing results (41-50 of 49) with videos related to

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The Journal of Physical Chemistry Letters|July 25, 2018
Discovering a Transferable Charge Assignment Model Using Machine LearningAndrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, et al.
Nature Communications|February 24, 2021
Automated discovery of a robust interatomic potential for aluminumJustin S Smith, Benjamin Nebgen, Nithin Mathew, et al.
Journal of Chemical Theory and Computation|February 2, 2024
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to ExperimentSakib Matin, Alice E A Allen, Justin Smith, et al.
Nature Reviews. Chemistry|April 28, 2023
Extending machine learning beyond interatomic potentials for predicting molecular propertiesNikita Fedik, Roman Zubatyuk, Maksim Kulichenko, et al.
Journal of Chemical Theory and Computation|July 14, 2026
Reactive Chemistry at the Unrestricted Coupled Cluster Level: High-Throughput Calculations for Training Machine Learning PotentialsAlice E A Allen, Rui Li, Sakib Matin, et al.
Nature Chemistry|March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialShuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Chemical Reviews|November 21, 2024
Data Generation for Machine Learning Interatomic Potentials and BeyondMaksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Nature Communications|February 16, 2020
Machine-learning-assisted insight into spin ice Dy<sub>2</sub>Ti<sub>2</sub>O<sub>7</sub>Anjana M Samarakoon, Kipton Barros, Ying Wai Li, et al.
Nature Communications|November 24, 2019
Anomalous magnetoresistance due to longitudinal spin fluctuations in a J<sub>eff</sub> = 1/2 Mott semiconductorLin Hao, Zhentao Wang, Junyi Yang, et al.
Pageof 5

Showing results (41-50 of 49) with videos related to

Sort By:
Pageof 5
You have reached the last page of results.This site can display upto 49 results.
The Journal of Physical Chemistry Letters|July 25, 2018
Discovering a Transferable Charge Assignment Model Using Machine LearningAndrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, et al.
Nature Communications|February 24, 2021
Automated discovery of a robust interatomic potential for aluminumJustin S Smith, Benjamin Nebgen, Nithin Mathew, et al.
Journal of Chemical Theory and Computation|February 2, 2024
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to ExperimentSakib Matin, Alice E A Allen, Justin Smith, et al.
Nature Reviews. Chemistry|April 28, 2023
Extending machine learning beyond interatomic potentials for predicting molecular propertiesNikita Fedik, Roman Zubatyuk, Maksim Kulichenko, et al.
Journal of Chemical Theory and Computation|July 14, 2026
Reactive Chemistry at the Unrestricted Coupled Cluster Level: High-Throughput Calculations for Training Machine Learning PotentialsAlice E A Allen, Rui Li, Sakib Matin, et al.
Nature Chemistry|March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialShuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Chemical Reviews|November 21, 2024
Data Generation for Machine Learning Interatomic Potentials and BeyondMaksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Nature Communications|February 16, 2020
Machine-learning-assisted insight into spin ice Dy<sub>2</sub>Ti<sub>2</sub>O<sub>7</sub>Anjana M Samarakoon, Kipton Barros, Ying Wai Li, et al.
Nature Communications|November 24, 2019
Anomalous magnetoresistance due to longitudinal spin fluctuations in a J<sub>eff</sub> = 1/2 Mott semiconductorLin Hao, Zhentao Wang, Junyi Yang, et al.
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