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Ben Nebgen

Scientist

Los Alamos National Laboratory

United States

Research Interests

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Professors in United States Professors in Condensed Matter Physics Condensed Matter Physics professors in United States

Articles (17)

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2026

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2023

Collaborators (10)

Boian Alexandrov

Research Professor

University of New Mexico

UNITED STATES

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Richard Messerly

Computational Scientist

Oak Ridge National Laboratory

UNITED STATES

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Dimiter N. Petsev

UNITED STATES

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Daniel P. Tabor

Associate Professor

Texas A&M University

UNITED STATES

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Nikolay Tkachenko

University of California, Berkeley

UNITED STATES

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Małgorzata Z Makoś

Assistant Professor

The University of Alabama

UNITED STATES

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Y Z

University of Michigan

UNITED STATES

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Olexandr Isayev

Assistant Professor

Carnegie Mellon University

UNITED STATES

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Manish Bhattarai

Staff Scientist

Los Alamos National Laboratory

UNITED STATES

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Brenden W Hamilton

Staff Scientist

Los Alamos National Laboratory

UNITED STATES

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Ben Nebgen

Research Interests

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Articles (17)

Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Mott vs Kondo: Influence of various density functional based methods on the Ce isostructural phase transition mechanism

Active-learned potentials reveal the interplay between the structure and dynamics in thorium molten salts

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need*

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential

Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Data Generation for Machine Learning Interatomic Potentials and Beyond

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Synergy of semiempirical models and machine learning in computational chemistry

Neural network atomistic potentials for global energy minima search in carbon clusters

Distributed non-negative RESCAL with automatic model selection for exascale data

Lightweight and effective tensor sensitivity for atomistic neural networks

Uncertainty-driven dynamics for active learning of interatomic potentials

Collaborators (10)

Boian Alexandrov

Richard Messerly

Dimiter N. Petsev

Daniel P. Tabor

Nikolay Tkachenko

Małgorzata Z Makoś

Y Z

Olexandr Isayev

Manish Bhattarai

Brenden W Hamilton

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