Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX)

Michael N. Sakano, Ahmed Hamed, Edward M. Kober, Nicolo Grilli, Brenden W. Hamilton, Md Mahbubul Islam, Marisol Koslowski, Alejandro Strachan*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

40 Citations (Scopus)


The response of high-energy-density materials to thermal or mechanical insults involves coupled thermal, mechanical, and chemical processes with disparate temporal and spatial scales that no single model can capture. Therefore, we developed a multiscale model for 1,3,5-trinitro-1,3,5-triazinane, RDX, where a continuum description is informed by reactive and nonreactive molecular dynamics (MD) simulations to describe chemical reactions and thermal transport. Reactive MD simulations under homogeneous isothermal and adiabatic conditions are used to develop a reduced-order chemical kinetics model. Coarse graining is done using unsupervised learning via non-negative matrix factorization. Importantly, the components resulting from the analysis can be interpreted as reactants, intermediates, and products, which allows us to write kinetics equations for their evolution. The kinetics parameters are obtained from isothermal MD simulations over a wide temperature range, 1200-3000 K, and the heat evolved is calibrated from adiabatic simulations. We validate the continuum model against MD simulations by comparing the evolution of a cylindrical hotspot 10 nm in diameter. We find excellent agreement in the time evolution of the hotspot temperature fields both in cases where quenching is observed and at higher temperatures for which the hotspot transitions into a deflagration wave. The validated continuum model is then used to assess the criticality of hotspots involving scales beyond the reach of atomistic simulations that are relevant to detonation initiation.

Original languageEnglish
Pages (from-to)9141-9155
Number of pages15
JournalJournal of Physical Chemistry A
Issue number44
Publication statusPublished - 5 Nov 2020

Bibliographical note

Funding Information:
This work was primarily supported by the US Office of Naval Research, Multidisciplinary University Research Initiatives (MURI) Program, Contract: N00014-16-1-2557. Program managers: Chad Stoltz and Kenny Lipkowitz. E.M.K. was supported through the High Explosives Grand Challenge program at the Los Alamos National Laboratory (Program Manager: Dan Borovina).

Publisher Copyright:
© 2020 American Chemical Society.


Dive into the research topics of 'Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX)'. Together they form a unique fingerprint.

Cite this