ArticleACS omega2026
A Deep Neural Network Potential Study on the Mechanical and Thermophysical Properties of β‑HMX Molecular Crystal.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Neural network potentials (NNPs) provide an efficient and accurate approach for predicting the mechanical and thermophysical properties of high-energy-density materials (HEDMs) under extreme temperature and pressure conditions, where experimental characterization is often limited and first-principles simulations become computationally demanding. In this work, an NNP for HEDMs is developed within a new active-learning framework that automatically eliminates structurally redundant configurations. The resulting NNP predicts the mechanical and thermophysical properties of β-cyclotetramethylene-tetranitramine (β-HMX) with density functional theory (DFT)-level accuracy while achieving orders-of-magnitude improvements in computational efficiency. The predicted bulk modulus of β-HMX at zero pressure agrees with experimental measurements within 13%, representing a substantial improvement over the classical molecular dynamics. Likewise, the volumetric thermal expansion coefficient α, constant-pressure heat capacity
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