The Impact of Artificial Intelligence and Machine Learning on Energetic Materials Research and Development

Artificial intelligence/machine learning (AI/ML) techniques are rapidly becoming a powerful tool in the arsenal of analysts, modelers, and designers of energetic materials.Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, discover linkages between design parameters and material properties, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics.For example, AI/ML approaches can already scan molecular structures and identify those with optimal physical and chemical properties, construct reaction kinetic schemes from atomistic simulation data, predict grain (meso ) scale mechanical and thermal response to a range of loading regimes, function as surrogate models to bridge scales in multiscale simulations, and learn and predict material properties and behavior at macro-scale.