Benjamin Nebgen

Los Alamos National Laboratory — United States

Materials Science · Physical Sciences

24h-index4.1kcitations133works5.82yr avg
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Research Topics

Machine Learning in Materials Science(73), Computational Drug Discovery Methods(31), Protein Structure and Dynamics(22), Spectroscopy and Quantum Chemical Studies(18), Various Chemistry Research Topics(14)

Publications133 total

Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
arXiv (Cornell University)·2026Open Access
Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
arXiv (Cornell University)·2026Open Access
Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
arXiv (Cornell University)·2026Open Access
Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
ArXiv.org·2026Open Access
Mott vs Kondo: Influence of various density functional based methods on the Ce isostructural phase transition mechanism
Journal of Applied Physics·2026Open Access
Mott vs Kondo: Influence of Various Density Functional Based Methods on the Ce Isostructural Phase Transition Mechanism
arXiv (Cornell University)·2025Open Access
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
arXiv (Cornell University)·2025· 1 citedOpen Access
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
UNC Libraries·2025Open Access
Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need<sup>*</sup>
Machine Learning Science and Technology·2025· 3 citedOpen Access
Discovering a Transferable Charge Assignment Model Using Machine Learning
UNC Libraries·2025Open Access
Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks
Physical Review Materials·2025· 1 citedOpen Access
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
arXiv (Cornell University)·2025Open Access
Breaking the Curse of Dimensionality: Solving Configurational Integrals for Crystalline Solids by Tensor Networks
arXiv (Cornell University)·2025Open Access
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
arXiv (Cornell University)·2025· 1 citedOpen Access
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification
Proceedings of the AAAI Conference on Artificial Intelligence·2025Open Access
Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies
2025Open Access
Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
2025· 5 citedOpen Access
Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential
Journal of Chemical Theory and Computation·2025· 2 cited
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
arXiv (Cornell University)·2025· 1 citedOpen Access
Topological Signatures of Adversaries in Multimodal Alignments
arXiv (Cornell University)·2025Open Access
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Frequent Co-authors

Sergei Tretiak(37), Nicholas Lubbers(30), Kipton Barros(26), Justin S. Smith(23), Olexandr Isayev(22), Adrián E. Roitberg(15), R.I. Zubatyuk(14), Richard A. Messerly(11), Andrew E. Sifain(8), Maksim Kulichenko(7), Ying Wai Li(7), Nikita Fedik(5), Justin S. Smith(5), Christian Devereux(4), Sebastian Fernández-Alberti(4), Andrey Y. Lokhov(4), Ryan B. Jadrich(4), Guoqing Zhou(4), Lyudmila V. Slipchenko(4), Tammie Nelson(3)