Work

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential

Year: 2026

Type: article

Abstract: Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. The efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0312621

Authors: Cong Huy Pham, Nir Goldman, Laurence E. Fried, Rebecca K. Lindsey, Jim A. Gaffney, Peer-Timo Bremer

Institutions: Lawrence Livermore National Laboratory, University of California, Davis, University of Michigan

Language: English

Topic: Energetic Materials and Combustion

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Laboratory Directed Research and Development

Awards: G14554683, G4299522278