A Bayesian Approach to Designing Microstructures and Processing Pathways for Tailored Material Properties
Submission Track: Papers
Submission Category: AI-Guided Design
Keywords: Inverse design, Generative modeling, Uncertainty quantification, Bayesian inference, Computational materials design, Microstructure
TL;DR: We present a Bayesian framework for the stochastic inversion of process-structure-property linkages, enabling the the exploration of novel structures, and providing a manufacturing pathway to realize such structures.
Abstract: Inverse problems are central to material design. While numerous studies have focused on designing microstructures by inverting structure-property linkages for various material systems, such efforts stop short of providing realizable paths to manufacture such structures. Accomplishing the dual task of designing a microstructure and a feasible manufacturing pathway to achieve a target property requires inverting the complete process-structure-property linkage. However, this inversion is complicated by a variety of challenges such as inherent microstructure stochasticity, high-dimensionality, and ill-conditioning of the inversion. In this work, we propose a Bayesian framework leveraging a lightweight flow-based generative approach for the stochastic inversion of the complete process-structure-property linkage. This inversion identifies a solution distribution in the processing parameter space; utilizing these processing conditions realizes materials with the target property sets. Our modular framework readily incorporates the output of stochastic forward models as conditioning variables for a flow-based generative model, thereby learning the complete joint distribution over processing parameters and properties. We demonstrate its application to the multi-objective task of designing processing routes of heterogeneous materials given target sets of bulk elastic moduli and thermal conductivities.
Submission Number: 34
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