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The Journal of Chemical Physics
|
September 15, 2023
Synergy of semiempirical models and machine learning in computational chemistry
Nikita Fedik, Benjamin Nebgen, Nicholas Lubbers, et al.
The Journal of Physical Chemistry Letters
|
July 1, 2021
The Rise of Neural Networks for Materials and Chemical Dynamics
Maksim Kulichenko, Justin S Smith, Benjamin Nebgen, 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.
Chemical Science
|
August 27, 2021
Predicting phosphorescence energies and inferring wavefunction localization with machine learning
Andrew E Sifain, Levi Lystrom, Richard A Messerly, et al.
The Journal of Chemical Physics
|
August 20, 2014
Vibronic coupling in asymmetric bichromophores: experimental investigation of diphenylmethane-d₅
Nathan R Pillsbury, Nathanael M Kidwell, Benjamin Nebgen, et al.
Journal of Chemical Theory and Computation
|
February 2, 2024
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Sakib Matin, Alice E A Allen, Justin Smith, et al.
Nature Communications
|
February 24, 2021
Automated discovery of a robust interatomic potential for aluminum
Justin S Smith, Benjamin Nebgen, Nithin Mathew, et al.
Chemical Reviews
|
February 11, 2020
Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials
Tammie R Nelson, Alexander J White, Josiah A Bjorgaard, et al.
Nature Reviews. Chemistry
|
April 28, 2023
Extending machine learning beyond interatomic potentials for predicting molecular properties
Nikita Fedik, Roman Zubatyuk, Maksim Kulichenko, et al.
Chemical Reviews
|
November 21, 2024
Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Page
of 3
Search research articles
Search
Showing results (11-20 of 21) with videos related to
Sort By:
Page
of 3
The Journal of Chemical Physics
|
September 15, 2023
Synergy of semiempirical models and machine learning in computational chemistry
Nikita Fedik, Benjamin Nebgen, Nicholas Lubbers, et al.
The Journal of Physical Chemistry Letters
|
July 1, 2021
The Rise of Neural Networks for Materials and Chemical Dynamics
Maksim Kulichenko, Justin S Smith, Benjamin Nebgen, 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.
Chemical Science
|
August 27, 2021
Predicting phosphorescence energies and inferring wavefunction localization with machine learning
Andrew E Sifain, Levi Lystrom, Richard A Messerly, et al.
The Journal of Chemical Physics
|
August 20, 2014
Vibronic coupling in asymmetric bichromophores: experimental investigation of diphenylmethane-d₅
Nathan R Pillsbury, Nathanael M Kidwell, Benjamin Nebgen, et al.
Journal of Chemical Theory and Computation
|
February 2, 2024
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Sakib Matin, Alice E A Allen, Justin Smith, et al.
Nature Communications
|
February 24, 2021
Automated discovery of a robust interatomic potential for aluminum
Justin S Smith, Benjamin Nebgen, Nithin Mathew, et al.
Chemical Reviews
|
February 11, 2020
Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials
Tammie R Nelson, Alexander J White, Josiah A Bjorgaard, et al.
Nature Reviews. Chemistry
|
April 28, 2023
Extending machine learning beyond interatomic potentials for predicting molecular properties
Nikita Fedik, Roman Zubatyuk, Maksim Kulichenko, et al.
Chemical Reviews
|
November 21, 2024
Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Page
of 3