EST. 1998

Center for
Engineering
Concepts
 Development (CECD)

A national resource for Energetic Materials Science and Manufacturing, based at the A. James Clark School of Engineering at the University of Maryland College Park.

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Recent Research Highlights

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Machine Learning

J. Appl. Phys. · 2023

Interpretable machine learning models shock compression from scarce data.

Balakrishnan, VanGessel, Barnes, and Chung train machine learning on sparse experimental data to recover the shock compression behavior of solids, where conventional fits run short of data.

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Phonon & Thermal Transport

Mater. Today Phys. · 2023

Molecular crystals show unusual thermal transport.

Liu and Chung uncover thermal transport in molecular crystals that departs from conventional expectations, with implications for how heat moves through energetic solids.

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Energetic Materials

J. Phys. Chem. A · 2021

Three-phonon pathways carry vibrational energy through crystalline RDX.

Kumar, VanGessel, Munday, and Chung map the 3-phonon scattering pathways that move vibrational energy through crystalline RDX, a step toward understanding how energetic crystals respond before initiation.

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Recent News

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  1. Event May 2026

    Maryland Energetics Innovation Hub breaks ground at Indian Head.

    CECD joined federal, state, and industry partners as ACMI and NSWC Indian Head Division broke ground on the $250 million hub, where the Center will help grow the next generation of energetics professionals.

  2. Publication 2023

    Machine learning recovers shock compression of solids from scarce data.

    Balakrishnan, VanGessel, Barnes, and Chung publish interpretable machine learning for the shock compression of solids in the Journal of Applied Physics.

  3. Publication 2022

    Selective phonon stimulation offers a route to tune thermal transport.

    Kumar and Chung describe a selective phonon stimulation mechanism for tuning thermal transport, published in ACS Omega.

  4. Result 2019

    Deep learning review maps the state of molecular design.

    Elton, Boukouvalas, Fuge, and Chung survey deep learning for molecular design in Molecular Systems Design & Engineering.

  5. Result 2018

    Machine learning predicts the properties of energetic materials.

    Elton, Boukouvalas, Butrico, Fuge, and Chung apply machine learning to predict energetic-materials properties in Scientific Reports.

Partners & Sponsors

Army Research Laboratory Office of Naval Research Naval Sea Systems Command Air Force Research Laboratory Office of Naval Intelligence Sandia National Laboratories Los Alamos National Laboratory Lawrence Livermore National Lab National Science Foundation U.S. Dept. of Homeland Security U.S. Dept. of Housing & Urban Dev. College of Southern Maryland
State of Maryland NEILOM Foundation Energetics Technology Center Areté Associates
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