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Materials-by-design has been historically challenging due to complexprocess-microstructure-property relations. Conventional analytical orsimulation-based approaches suffer from low accuracy or long computational timeand poor transferability, further limiting their applications in solving theinverse material design problem. Here, we establish a deep learning and geneticalgorithm framework that integrates forward prediction and inverse exploration.This framework provides an end-to-end solution to achieve application-specificmechanical properties by microstructure optimization. In this study, we selectthe widely used Ti-6Al-4V to demonstrate the effectiveness of this framework bytailoring its microstructure and achieving various yield strength and elasticmodulus across a large design space, while minimizing the stress concentrationfactor. Compared with conventional methods, our framework is efficient,versatile, and readily transferrable to other materials and properties. Pairedwith additive manufacturing's potential in controlling local microstructuralfeatures, our method has far-reaching potential for accelerating thedevelopment of application-specific, high-performing materials.

Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework