Tuning Mechanical Properties in Polycrystalline Solids Using a Deep Generative Framework

Abstract

A framework for inverse design of tuning mechanical properties of polycrystalline brittle materials is presented using artificial intelligence (AI). Crystalline solids, which often exhibit distinct mechanical properties at different orientations, can be used as building blocks for polycrystalline composites. However, the design space of geometry and crystal misorientations is typically intractable, and all possible solutions cannot be discovered using experiment or numerical simulation. Herein, a framework using deep learning (DL) alongside a genetic algorithm (GA) is adopted to generate composite polycrystalline materials, whereas the raw material is brittle and sensitive to crystalline orientation, to achieve distinct mechanical properties in various combinatorial designs. The DL model, trained by full-atomistic simulations of crystals with different orientations, evolves autonomously to yield a desirable range of target toughness values, exemplified in maximizing and minimizing toughness, which are validated by molecular dynamics (MD) simulations. It is found that higher crystal misorientations are preferred for high toughness, as opposed to lower overall misorientations for low-toughness designs. Notably, this shows that a mechanism can be extracted from the AI algorithm. This materiomics method may ultimately change the way nanomaterials are designed, and can be applied to de novo biomaterial design, architected materials, and bioinspired structural materials.