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Kipton Barros

Showing results (31-40 of 48) with videos related to

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Nature Computational Science|January 4, 2024
Uncertainty-driven dynamics for active learning of interatomic potentialsMaksim Kulichenko, Kipton Barros, Nicholas Lubbers, et al.
Scientific Data|May 3, 2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for moleculesJustin S Smith, Roman Zubatyuk, Benjamin Nebgen, et al.
Journal of Chemical Theory and Computation|April 2, 2021
Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning PerspectiveJoshua Finkelstein, Justin S Smith, Susan M Mniszewski, et al.
The Journal of Chemical Physics|July 9, 2021
Machine learned Hückel theory: Interfacing physics and deep neural networksTetiana 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 learningJustin 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 NetworksBenjamin Nebgen, Nicholas Lubbers, Justin S Smith, et al.
The Journal of Chemical Physics|September 15, 2023
Synergy of semiempirical models and machine learning in computational chemistryNikita Fedik, Benjamin Nebgen, Nicholas Lubbers, et al.
Journal of Chemical Theory and Computation|October 1, 2021
Quantum-Based Molecular Dynamics Simulations Using Tensor CoresJoshua Finkelstein, Justin S Smith, Susan M Mniszewski, et al.
The Journal of Physical Chemistry Letters|July 1, 2021
The Rise of Neural Networks for Materials and Chemical DynamicsMaksim Kulichenko, Justin S Smith, Benjamin Nebgen, et al.
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.
Pageof 5

Showing results (31-40 of 48) with videos related to

Sort By:
Pageof 5
Nature Computational Science|January 4, 2024
Uncertainty-driven dynamics for active learning of interatomic potentialsMaksim Kulichenko, Kipton Barros, Nicholas Lubbers, et al.
Scientific Data|May 3, 2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for moleculesJustin S Smith, Roman Zubatyuk, Benjamin Nebgen, et al.
Journal of Chemical Theory and Computation|April 2, 2021
Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning PerspectiveJoshua Finkelstein, Justin S Smith, Susan M Mniszewski, et al.
The Journal of Chemical Physics|July 9, 2021
Machine learned Hückel theory: Interfacing physics and deep neural networksTetiana 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 learningJustin 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 NetworksBenjamin Nebgen, Nicholas Lubbers, Justin S Smith, et al.
The Journal of Chemical Physics|September 15, 2023
Synergy of semiempirical models and machine learning in computational chemistryNikita Fedik, Benjamin Nebgen, Nicholas Lubbers, et al.
Journal of Chemical Theory and Computation|October 1, 2021
Quantum-Based Molecular Dynamics Simulations Using Tensor CoresJoshua Finkelstein, Justin S Smith, Susan M Mniszewski, et al.
The Journal of Physical Chemistry Letters|July 1, 2021
The Rise of Neural Networks for Materials and Chemical DynamicsMaksim Kulichenko, Justin S Smith, Benjamin Nebgen, et al.
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.
Pageof 5