Effect of Constituent Materials on Composite Performance: Exploring Design Strategies via Machine Learning

Abstract Nature assembles a range of biological composites with remarkable mechanical properties despite being composed of relatively weak polymeric and ceramic components. However, the architectures of biomaterials cannot be considered as optimal designs for engineering applications since biomaterials are constantly evolving for multiple functions beyond carrying external loading. Here, it is aimed to develop an intelligent approach to design superior composites from scratch—starting from constituent materials. A systematic computational investigation of the effect of constituent materials (assumed to be perfectly brittle) on the behavior of composites using an integrated approach combining finite element method, molecular dynamics, and machine learning (ML) is reported. It is demonstrated that instead of using brute‐force methods, machine learning is a much more efficient approach and can generate optimal designs with similar performance to those obtained from an exhaustive search. Furthermore, it is shown that the toughening and strengthening mechanism observed in composites at the continuum‐scale by combining stiff and soft constituents is valid for nanomaterials as well. Results show that high‐performing designs of graphene nanocomposites can be generated using our ML approach. This novel ML‐based design framework can be applied to other material systems to study a variety of structure–property relationships over several length‐scales.