Keywords: deep learning, materials informatics, big data, PSPP relationships
Summary:The growing application of data-driven analytics in materials science has led to the rise and popularity of the relatively new field of materials informatics. Within the arena of data analytics, in recent years deep learning has emerged as a game-changing technique, which has enabled numerous real-world applications such as self-driving cars. In this talk, I would present some of our recent works at the intersection of deep learning and materials informatics, for exploring processing-structure-property-performance (PSPP) linkages in materials. Illustrative examples include learning the chemistry of materials using only elemental composition, learning multiscale homogenization and localization linkages in high-contrast composites, deep adversarial learning for microstructure design, deep learning for EBSD indexing, and deep transfer learning for small materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in data science approaches offers lot of promise to accelerate the discovery, design, and deployment of next-generation materials.