Investigating 3, 4-bis (3-nitrofurazan-4-yl) furoxan detonation with a rapidly tuned density functional tight binding model
RK Lindsey, S Bastea, N Goldman… - The Journal of Chemical …, 2021 - pubs.aip.org
We describe a machine learning approach to rapidly tune density functional tight binding
models for the description of detonation chemistry in organic molecular materials. Resulting
models enable simulations on the several 10s of ps scales characteristic to these processes,
with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3, 4-
bis (3-nitrofurazan-4-yl) furoxan, a hydrogen-free energetic material known to form onion-
like nanocarbon particulates following detonation. We find that the ensuing chemistry is …
models for the description of detonation chemistry in organic molecular materials. Resulting
models enable simulations on the several 10s of ps scales characteristic to these processes,
with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3, 4-
bis (3-nitrofurazan-4-yl) furoxan, a hydrogen-free energetic material known to form onion-
like nanocarbon particulates following detonation. We find that the ensuing chemistry is …
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