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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.27325 |
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| _version_ | 1866914605343703040 |
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| author | Gonzalez-Zapata, Simon Pantoya, Aidan Li, Chunyu Koslowski, Marisol Strachan, Alejandro |
| author_facet | Gonzalez-Zapata, Simon Pantoya, Aidan Li, Chunyu Koslowski, Marisol Strachan, Alejandro |
| contents | The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional multiscale models fail due to the lack of scale separation. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a convolutional deep neural network. Trained on MD simulations of shock propagation through complex microstructures, MISTnetX captures shock-microstructure interactions, hotspot formation, and the transition to deflagration, supplying critical sub-grid information to FE simulations of mechanics, shocks, thermal transport, and chemistry. Applied to a synthetic but realistic nanostructured plastic-bonded RDX composite, MISTnetX enables parameter-free prediction of the full run-to-detonation transition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27325 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction Gonzalez-Zapata, Simon Pantoya, Aidan Li, Chunyu Koslowski, Marisol Strachan, Alejandro Materials Science The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional multiscale models fail due to the lack of scale separation. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a convolutional deep neural network. Trained on MD simulations of shock propagation through complex microstructures, MISTnetX captures shock-microstructure interactions, hotspot formation, and the transition to deflagration, supplying critical sub-grid information to FE simulations of mechanics, shocks, thermal transport, and chemistry. Applied to a synthetic but realistic nanostructured plastic-bonded RDX composite, MISTnetX enables parameter-free prediction of the full run-to-detonation transition. |
| title | Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.27325 |