Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis

Mingjie Wen; Jiahe Han; Zihan Zhou; Hao Wang; Xueyong Guo; Qingzhao Chu; Dongping Chen

cond-mat.mtrl-sciOct 30, 2026

Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis

Authors: Mingjie Wen, Jiahe Han, Zihan Zhou, Hao Wang, Xueyong Guo, Qingzhao Chu, Dongping Chen

Organizations: State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China

Abstract

Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways. Here, we develop EMFF-MACE, a reactive machine-learning potential for C/H/N/O energetic materials, enabling thermolysis simulations of 20 materials and shock simulations of 10 on a common potential energy surface. Comparisons at matched normalized reaction progress reveal divergent chemical states and local structural environments. Mean reaction-state similarity decreases from 94.0% at 20% progress to 25.9% at trajectory endpoints, while 47.1-92.6% of shock local environments lie outside corresponding material-specific thermolysis reference domains. Structural and reaction-network analyses reveal enhanced intermolecular reorganization and additional association and rearrangement branches under shock loading, despite partially shared early decomposition chemistry. These findings show that loading history influences the structural states and subsequent reaction pathways accessed during decomposition, rather than merely the rate of thermolysis.

Materials Science

Materials science studies how composition, structure, processing, and defects determine material properties and how to design useful materials.

See all 322 papers

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
Apr 9, 2026R. Seaton Ullberg, Megan C. Davis, Jeremy N. Schroeder +5Materials Property Prediction Materials Science
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA · Weapon Stockpile Modernization Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials
Aug 24, 2026Megan C. Davis, R. Seaton Ullberg, Jeremy N. Schroeder +5Materials Property Prediction Molecular Property Prediction
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States · Department of Mechanical and Aerospace Engineering, Texas Tech University, Lubbock, TX 79409, United States · Weapon Stockpile Modernization Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States
The High Explosives and Affected Targets (HEAT) Dataset
Apr 20, 2026Bryan Kaiser, Kyle Hickmann, Sharmistha Chakrabarti +6Surrogate Modeling Synthetic Benchmark Generation
Los Alamos National Laboratory

Apr 9, 2026J/K move · Enter open · S save

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

R. Seaton Ullberg, Megan C. Davis, Jeremy N. Schroeder, Andrew H. Salij+4

Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA · Weapon Stockpile Modernization Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Materials Property Prediction Materials Science Molecular Property Prediction

Open paper AskPDF arXiv

The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches remain slow and expensive while computational alternatives require accurate material property inputs that are often costly to obtain, limiting their ability to efficiently predict detonation performance across a vast chemical space. We address this challenge through an active learning strategy that integrates density functional theory calculations, thermochemical modeling, message-passing neural networks, and Bayesian optimization. The resulting high-throughput workflow iteratively expands the training dataset by selecting new molecules in a targeted manner that balances the exploration of broad chemical space with the exploitation of promising high-performing candidates. This approach yields the largest publicly available database of potential CHNO explosives drawn from an initial pool of more than 70 billion candidates and a generalizable surrogate model capable of accurately predicting detonation performance (R

^2

> 0.98). Feature importance analysis on this largest-to-date dataset reveals that oxygen balance is the dominant driver of detonation performance, complemented by contributions from local electronic structure, density, and the presence of specific functional groups. Cheminformatics analysis highlights how energetic materials with similar performance metrics tend to cluster in distinct chemical spaces offering a clearer direction for future synthesis studies. Together, the surrogate model, database, and resulting chemical insights provide a valuable foundation for high-throughput screening and targeted discovery of new energetic materials spanning diverse and previously unexplored regions of chemical space.

Abstract

Materials Science

Explore similar work

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials

The High Explosives and Affected Targets (HEAT) Dataset