Addressing the grand challenges of materials-by-design

Our group focuses on developing novel informatics approaches to accelerate the search and discovery of new materials. Our capabilities include establishing quantitative structure-property relationships for bulk, thin films, and heterostructures (to name a few) and guiding experiments/computations towards promising regions in the vast design space using density functional theory (DFT), machine learning (with an emphasis on uncertainty quantification), Bayesian learning, and optimal learning methods. Thus, we seek to address the grand challenges of materials-by-design from the viewpoint of learning from data (experimental and/or computational). Further, we also leverage the materials knowledge (whenever possible) and seek to uncover meaningful physical insights from the vast and high-dimensional search space. Our overarching goal is to build a data- and knowledge-based informatics platform for a wide variety of problems in materials science and engineering.