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The sensitivity of heterogeneous energetic (HE) materials (propellants,explosives, and pyrotechnics) is critically dependent on their microstructure.Initiation of chemical reactions occurs at hot spots due to energy localizationat sites of porosities and other defects. Emerging multi-scale predictivemodels of HE response to loads account for the physics at the meso-scale, i.e.at the scale of statistically representative clusters of particles and otherfeatures in the microstructure. Meso-scale physics is infused inmachine-learned closure models informed by resolved meso-scale simulations.Since microstructures are stochastic, ensembles of meso-scale simulations arerequired to quantify hot spot ignition and growth and to develop models formicrostructure-dependent energy deposition rates. We propose utilizinggenerative adversarial networks (GAN) to spawn ensembles of syntheticheterogeneous energetic material microstructures. The method generatesqualitatively and quantitatively realistic microstructures by learning fromimages of HE microstructures. We show that the proposed GAN method also permitsthe generation of new morphologies, where the porosity distribution can becontrolled and spatially manipulated. Such control paves the way for the designof novel microstructures to engineer HE materials for targeted performance in amaterials-by-design framework.

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials