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

Abstract

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 localization at sites of porosities and other defects. Emerging multi-scale predictive models 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 other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework.

References in corpus (4)

• Microstructural Materials Design via Deep Adversarial Learning Methodology
• Microstructure synthesis using style-based generative adversarial network
• Modeling meso-scale energy localization in shocked HMX, Part I: machine- learned surrogate model for effect of loading and void size
• Structure-Property Linkage in Shocked Multi-Material Flows Using A Level-Set Based Eulerian Image-To-Computation Framework

Cited by in corpus (11)

• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Overview: Computer vision and machine learning for microstructural characterization and analysis
• Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
• PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
• AtomVision: A machine vision library for atomistic images
• 3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models
• A Metric on the Polycrystalline Microstructure State Space
• A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials