Discipline leading Energetic Material (EM), Reactive Material (RM), and Machine Learning (ML) subject matter experts (SME) will present research emphasizing how deeper understanding of EM/RM physics, chemistry, and kinetics can be achieved when coordinated efforts are brought to bear on targeted knowledge gaps. For example, physics- and chemistry-based modeling and simulation (M&S) effort, when anchored to sub-scale experimental data, (relative to a technological system), provide a robust basis for confident semi-empirical explosives safety and performance prediction. Larger-scale system performance tests verify and validate (V&V) predictions and illuminate critical gaps in science-based M&S approaches. V&V empirical tests are challenging and costly and, they may (sparsely at best) or may not bracket conditions where future system-scale data are required. Moreover, the task of uniquely attributing V&V results to fundamental chemical physics phenomena, at nano and meso spatial scales, is frequently fraught; questions abound concerning uniqueness in attempts to relate macro-scale observations to the underlying physical and chemical mechanisms (i.e., the uniqueness of solutions to the inverse problem). However, rapidly emerging advances in ML techniques hold promise for substantially alleviating this decades-long impediment to robust and reliable science-based predictive capability. ML publications account seemingly miraculous numerical routes for identifying, characterizing, and quantifying couplings among chemistry, meso-scale chemical physics and engineering mechanics, and experimentally measured observation. Energetic materials and RM research and engineering are on the cusp of disruptive (transformational) change initiated by data-assimilation in M&S codes including V&V data generation, and the predictive capabilities of neural networks, genetic algorithms, deep learning, and other ML approaches. The 2020 EM GRC/GRS are structured using traditional topic areas to formally acknowledge we are truly at a confluence: Acknowledgement will yield enhanced EM and RM knowledge development paths created by collaboration of leading scientists and machine learning SMEs.
The conference will consist of nine sessions, on the topics listed below. The conference chair is currently developing their preliminary program, which will include the names of the invited speakers and discussion leaders for each of these sessions. Please check back regularly for updates to this information.
- Thermodynamics and Kinetics: Tailoring the Energy of Energetic Materials
- Micro-Scale Models: Ignition and Thermal Transport
- Meso-Scale Models: Science and Machine Learning-Based SDT and Deflagration
- Machine Learning: Genetic Algorithms in Support of Chemical Prescriptions
- Energetic and Reactive Materials Synthesis: Organic, Inorganic and In Silico
- Additive Manufacturing: Optimizing Performance by Design
- Thermo-Vibrational Dynamics: Learning from Emergent Response
- Inter-Fireball Dynamics
- Intelligently Designed Propellants