In this age of “big data”, large-scale experimental and simulation data is increasingly becoming available in all fields of science, and materials science is no exception to it. Our ability to collect and store this data has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need to use of advanced data science approaches in materials science is also recognized by the Materials Genome Initiative (MGI), further promoting the emerging field of materials informatics.
In this talk, I would present some of our recent works employing state-of-the-art data analytics for exploring processing-structure-property-performance (PSPP) linkages in materials, both in terms of forward models (e.g. predicting property for a given material) and inverse models (e.g. discovering materials that possess a desired property). Some examples include developing models for predicting fatigue strength of steel alloys, data-driven discovery of stable compounds, and microstructure optimization of a magnetostrictive Fe-Ga alloy. I will also demonstrate some online web-tools we have developed that deploy machine learning models to predict materials properties.
Such data-driven analytics can significantly accelerate prediction of material properties, which in turn can accelerate the optimization process and thus help realize the dream of rational materials design. The increasingly availability of materials databases along with groundbreaking advances in data science approaches offers lot of promise to successfully realize the goals of MGI, and aid in the discovery, design, and deployment of next-generation materials.