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Benjamin Nebgen

united states department of energy (doe)

24H-index
132Paper Count
4.2KCitation Count
Published Papers 36
Publication Date
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Mott vs Kondo: Influence of various density functional based methods on the Ce isostructural phase transition mechanism
errJournal of Applied Physics
IF2.5
err2026-02-07
err0
PREAI
errHamilton, Brenden W.; Munoz, Alexander R.; Jones, Travis E.; Nebgen, Benjamin T.
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Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need*
errMachine Learning: Science and Technology
IF0
err2025-09-29
err0
errOAAI
errMitchell Messerly; Sakib Matin; Alice E A Allen; Benjamin Nebgen; Kipton Barros; Justin S Smith; Nicholas Lubbers; Richard Messerly
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Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials
errDigital Discovery
IF5.6
err2025-08-07
err0
errOAAI
errSakib Matin; Alice E. A. Allen; Emily Shinkle; Aleksandra Pachalieva; Galen T. Craven; Benjamin Nebgen; Justin S. Smith; Richard Messerly; Ying Wai Li; Sergei Tretiak; Kipton Barros; Nicholas Lubbers
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Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential
errJOURNAL OF CHEMICAL THEORY AND COMPUTATION
IF5.5
err2025-03-01
err0
PREAI
errLi, Cheng-Han; Kaymak, Mehmet Cagri; Kulichenko, Maksim; Lubbers, Nicholas; Nebgen, Benjamin T.; Tretiak, Sergei; Finkelstein, Joshua; Tabor, Daniel P.; Niklasson, Anders M. N.
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Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation
errJOURNAL OF CHEMICAL INFORMATION AND MODELING
IF5.3
err2025-01-28
err0
PREAI
errdos Santos, Luan G. F.; Nebgen, Benjamin T.; Allen, Alice E. A.; Hamilton, Brenden W.; Matin, Sakib; Smith, Justin S.; Messerly, Richard A.
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Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies
errDIGITAL DISCOVERY
IF5.6
err2025-01-01
err0
errOAAI
errFedik, Nikita; Li, Wei; Lubbers, Nicholas; Nebgen, Benjamin; Tretiak, Sergei; Li, Ying Wai
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Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
errNATURE CHEMISTRY
IF20.2
err2024-03-07
err22
errOAAI
errZhang, Shuhao; Makos, Malgorzata Z.; Jadrich, Ryan B.; Kraka, Elfi; Barros, Kipton; Nebgen, Benjamin T.; Tretiak, Sergei; Isayev, Olexandr; Lubbers, Nicholas; Messerly, Richard A.; Smith, Justin S.
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Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
errJOURNAL OF CHEMICAL THEORY AND COMPUTATION
IF5.5
err2024-02-02
err10
errOAAI
errMatin, Sakib; Allen, Alice E. A.; Smith, Justin; Lubbers, Nicholas; Jadrich, Ryan B.; Messerly, Richard; Nebgen, Benjamin; Li, Ying Wai; Tretiak, Sergei; Barros, Kipton
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Synergy of semiempirical models and machine learning in computational chemistry
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2023-09-15
err8
errOAAI
errFedik, Nikita; Nebgen, Benjamin; Lubbers, Nicholas; Barros, Kipton; Kulichenko, Maksim; Li, Ying Wai; Zubatyuk, Roman; Messerly, Richard; Isayev, Olexandr; Tretiak, Sergei
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Distributed non-negative RESCAL with automatic model selection for exascale data
errJOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
IF4
err2023-09-01
err4
errOAAI
errBhattarai, Manish; Kharat, Namita; Boureima, Ismael; Skau, Erik; Nebgen, Benjamin; Djidjev, Hristo; Rajopadhye, Sanjay; Smith, James P.; Alexandrov, Boian
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Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation
errJOURNAL OF CHEMICAL THEORY AND COMPUTATION
IF5.5
err2023-05-10
err1
errOAAI
errKulichenko, Maksim; Barros, Kipton; Lubbers, Nicholas; Fedik, Nikita; Zhou, Guoqing; Tretiak, Sergei; Nebgen, Benjamin; Niklasson, Anders M. N.
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Lightweight and effective tensor sensitivity for atomistic neural networks
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2023-05-09
err9
errOAAI
errChigaev, Michael; Smith, Justin S. S.; Anaya, Steven; Nebgen, Benjamin; Bettencourt, Matthew; Barros, Kipton; Lubbers, Nicholas
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Uncertainty-driven dynamics for active learning of interatomic potentials
errNATURE COMPUTATIONAL SCIENCE
IF18.3
err2023-03-06
err39
errOAAI
errKulichenko, Maksim; Barros, Kipton; Lubbers, Nicholas; Li, Ying Wai; Messerly, Richard; Tretiak, Sergei; Smith, Justin S.; Nebgen, Benjamin
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errSave
Extending machine learning beyond interatomic potentials for predicting molecular properties (vol 6, pg 653, 2022)
errNATURE REVIEWS CHEMISTRY
IF51.7
err2022-11-16
err0
errOAAI
errFedik, Nikita; Zubatyuk, Roman; Kulichenko, Maksim; Lubbers, Nicholas; Smith, Justin S.; Nebgen, Benjamin; Messerly, Richard; Li, Ying Wai; Boldyrev, Alexander I.; Barros, Kipton; Isayev, Olexandr; Tretiak, Sergei
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Extending machine learning beyond interatomic potentials for predicting molecular properties
errNATURE REVIEWS CHEMISTRY
IF51.7
err2022-08-25
err65
PREAI
errFedik, Nikita; Zubatyuk, Roman; Kulichenko, Maksim; Lubbers, Nicholas; Smith, Justin S.; Nebgen, Benjamin; Messerly, Richard; Li, Ying Wai; Boldyrev, Alexander, I; Barros, Kipton; Isayev, Olexandr; Tretiak, Sergei
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Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics
errPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
IF9.1
err2022-07-01
err29
errOAAI
errZhou, Guoqing; Lubbers, Nicholas; Barros, Kipton; Tretiak, Sergei; Nebgen, Benjamin
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Teaching a neural network to attach and detach electrons from molecules
errNATURE COMMUNICATIONS
IF15.7
err2021-08-11
err55
errOAAI
errZubatyuk, Roman; Smith, Justin S.; Nebgen, Benjamin T.; Tretiak, Sergei; Isayev, Olexandr
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Machine learned Huckel theory: Interfacing physics and deep neural networks
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2021-06-25
err30
errOAAI
errZubatiuk, Tetiana; Nebgen, Benjamin; Lubbers, Nicholas; Smith, Justin S.; Zubatyuk, Roman; Zhou, Guoqing; Koh, Christopher; Barros, Kipton; Isayev, Olexandr; Tretiak, Sergei
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Automated discovery of a robust interatomic potential for aluminum
errNATURE COMMUNICATIONS
IF15.7
err2021-02-23
err62
errOAAI
errSmith, Justin S.; Nebgen, Benjamin; Mathew, Nithin; Chen, Jie; Lubbers, Nicholas; Burakovsky, Leonid; Tretiak, Sergei; Nam, Hai Ah; Germann, Timothy; Fensin, Saryu; Barros, Kipton
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Predicting phosphorescence energies and inferring wavefunction localization with machine learning
errCHEMICAL SCIENCE
IF7.4
err2021-01-01
err17
errOAAI
errSifain, Andrew E.; Lystrom, Levi; Messerly, Richard A.; Smith, Justin S.; Nebgen, Benjamin; Barros, Kipton; Tretiak, Sergei; Lubbers, Nicholas; Gifford, Brendan J.
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Research Directions

No research directions
Co-authors
Cooperation Journals
T
Thomas Frauenheim
H-index: 100 · Papers: 939
O
Oleg V. Prezhdo
H-index: 98 · Papers: 803
M
M. D. Fayer
H-index: 92 · Papers: 654
S
Sergei Tretiak
H-index: 88 · Papers: 737
A
Alexander I. Boldyrev
H-index: 87 · Papers: 558
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