Research Area

The science of energetic materials from molecules to performance

How does a molecular crystal respond when struck, shocked, heated, or sparked? CECD's energetic-materials science program answers that question across length and time scales, combining first-principles chemistry, mesoscale defect dynamics, and large-scale validation against experimental data from federal laboratories.

Material systems

RDX · HMX · CL-20 · TATB

Length scales

Å to mm

Time scales

fs to μs

Methods

DFT · MD · Coarse-grain · ML

Representative work

Recent investigations

Selected studies that capture the program's breadth. Vibrational energy transfer, ML-driven property prediction, and chemically driven ferroelectricity in energetic crystals.

See Publications
J. Chem. Phys. 2024 Liu, Batyrev, Byrd, Chung

Vibrational energy transfer in RDX from selective IR excitation

A non-equilibrium kinetic model resolves how IR photons drive mode-specific vibrational populations in RDX. Direct insight into how energy localizes before bond breaking.

Nature Comms 2022 Hu, Gottfried, Wu, et al.

Releasing chemical energy in spatially programmed ferroelectrics

A new chemistry of energetic molecular ferroelectrics in which ferroelectric switching controls how, and where. Chemical energy is released.

Sci. Reports 2018 Elton, Boukouvalas, Butrico, Fuge, Chung

Predicting properties of energetic materials with machine learning

A foundational study showing that supervised ML models trained on curated experimental data can predict density, detonation velocity, and impact sensitivity from molecular structure alone.

Capabilities

What we bring to the work

The program brings together specialized capabilities developed over two decades, and is integrated with the wider CECD portfolio in mechanics, manufacturing, and diagnostics.

First-principles chemistry

DFT, AIMD, and hybrid functionals for accurate reaction energetics in molecular crystals.

Classical & reactive MD

Large-scale molecular dynamics with refined reactive force fields for shock-to-detonation studies.

Generative & supervised ML

Models trained on curated property databases for inverse design and rapid screening.

Coupled experiment

Tight loops with ARL, NSWC IHEODTD, LANL, and Sandia diagnostic facilities for validation.

Partner on Energetic Materials Science

Inquiries about sponsored research, graduate training, or technology transitions in this area can be directed to the center.