Publications by authors named "Benjamin Nebgen"

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Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential.
Cheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, Nicholas Lubbers, Benjamin T Nebgen

J Chem Theory Comput· April 2025

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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, Brenden W Hamilton, Sakib Matin

J Chem Inf Model· February 2025

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Data Generation for Machine Learning Interatomic Potentials and Beyond.
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, Justin S Smith, Kipton Barros

Chem Rev· December 2024

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Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.
Shuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, Elfi Kraka, Kipton Barros, Benjamin T Nebgen

Nat Chem· May 2024

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Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment.
Sakib Matin, Alice E A Allen, Justin Smith, Nicholas Lubbers, Ryan B Jadrich, Benjamin Nebgen

J Chem Theory Comput· February 2024

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Synergy of semiempirical models and machine learning in computational chemistry.
Nikita Fedik, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Maksim Kulichenko

J Chem Phys· September 2023

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Neural network atomistic potentials for global energy minima search in carbon clusters.
Nikolay V Tkachenko, Anastasiia A Tkachenko, Benjamin Nebgen, Sergei Tretiak, Alexander I Boldyrev

Phys Chem Chem Phys· August 2023

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Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation.
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Nikita Fedik, Guoqing Zhou, Benjamin Nebgen

J Chem Theory Comput· June 2023

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Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles.
Adela Habib, Nicholas Lubbers, Sergei Tretiak, Benjamin Nebgen

J Phys Chem A· May 2023

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Lightweight and effective tensor sensitivity for atomistic neural networks.
Michael Chigaev, Justin S Smith, Steven Anaya, Benjamin Nebgen, Matthew Bettencourt

J Chem Phys· May 2023

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Uncertainty-driven dynamics for active learning of interatomic potentials.
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard Messerly, Benjamin Nebgen

Nat Comput Sci· March 2023

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Extending machine learning beyond interatomic potentials for predicting molecular properties.
Nikita Fedik, Roman Zubatyuk, Maksim Kulichenko, Nicholas Lubbers, Justin S Smith, Benjamin Nebgen

Nat Rev Chem· September 2022

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Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.
Guoqing Zhou, Nicholas Lubbers, Kipton Barros, Sergei Tretiak, Benjamin Nebgen

Proc Natl Acad Sci U S A· July 2022

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Teaching a neural network to attach and detach electrons from molecules.
Roman Zubatyuk, Justin S Smith, Benjamin T Nebgen, Sergei Tretiak, Olexandr Isayev

Nat Commun· August 2021

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Predicting phosphorescence energies and inferring wavefunction localization with machine learning.
Andrew E Sifain, Levi Lystrom, Richard A Messerly, Justin S Smith, Benjamin Nebgen

Chem Sci· August 2021

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The Rise of Neural Networks for Materials and Chemical Dynamics.
Maksim Kulichenko, Justin S Smith, Benjamin Nebgen, Ying Wai Li, Nikita Fedik

J Phys Chem Lett· July 2021

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Machine learned Hückel theory: Interfacing physics and deep neural networks.
Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, Justin S Smith, Roman Zubatyuk

J Chem Phys· June 2021

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Automated discovery of a robust interatomic potential for aluminum.
Justin S Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers

Nat Commun· February 2021

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Intermolecular conical intersections in molecular aggregates.
Antonietta De Sio, Ephraim Sommer, Xuan Trung Nguyen, Lynn Groß, Duško Popović, Benjamin Tyler Nebgen

Nat Nanotechnol· January 2021

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NEXMD Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations.
Walter Malone, Benjamin Nebgen, Alexander White, Yu Zhang, Huajing Song

J Chem Theory Comput· September 2020

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The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules.
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros

Sci Data· May 2020

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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, Andrew E Sifain, Yu Zhang, Benjamin Nebgen

Chem Rev· February 2020

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The effects of site asymmetry on near-degenerate state-to-state vibronic mixing in flexible bichromophores.
Nathanael M Kidwell, Benjamin Nebgen, Lyudmila V Slipchenko, Timothy S Zwier

J Chem Phys· August 2019

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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux

Nat Commun· July 2019

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Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.
Benjamin Nebgen, Nicholas Lubbers, Justin S Smith, Andrew E Sifain, Andrey Lokhov

J Chem Theory Comput· September 2018

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