Work

Designing a quantum-accurate machine-learning potential to enable large-scale simulations of deuterium under shock

Year: 2025

Type: article

Abstract: Large-scale molecular dynamics of deuterium under shock can elucidate kinetic processes vital to the target design in inertial confinement fusion and high-energy-density experiments. However, modeling the complex evolution of this material from an insulating molecular state at ambient pressure to an ionized, atomic fluid under strong shock is beyond the capability of simple pair and even bond order potentials. We thus train a quantum-accurate and broadly transferable machine-learning interatomic potential for deuterium using the Chebyshev Interaction Model for Efficient Simulations framework. We show that due to an improved description of the molecular-to-atomic transition, our model is able to better reproduce the ab initio equation of state, radial distribution functions, and principal Hugoniot than bond order potentials. This represents an important step toward large-scale quantum-accurate and nonequilibrium simulations of complicated systems under dynamic changes including phase transitions.

Source: Physics of Plasmas

DOI: 10.1063/5.0254638

Authors: J. X. D'Souza, S. X. Hu, D. I. Mihaylov, V. V. Karasiev, V. N. Goncharov, S. Zhang

Institutions: Laboratory for Laser Energetics, University of Rochester

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, U.S. Department of Energy, University of Rochester, National Nuclear Security Administration

Awards: G1116528938, G4202668658, G5484809798