Professor John Gregoire delivered an A3MD Distinguished Seminar entitled: “Accelerating discovery of solar fuels materials with high throughput experiments and artificial intelligence”
The large data flux of high throughput experiments naturally presents opportunities for data science. Addressing the most pressing materials discovery challenges with these techniques requires design of experiments and algorithms in the context of the materials physics and chemistry that are pertinent to the target technology. The corresponding quest for materials that harvest solar energy to generate chemical fuels has led to research problems that challenge the state of the art in artificial intelligence. The ensuing efforts to generate new algorithms and modalities of research include 1) the development of deep reasoning networks that incorporate physics rules in machine learning, which is critical for automating crystal structure phase mapping; 2) the incorporation of hierarchical correlation learning in multi-property prediction, which conditions models to facilitate prediction in never-before-seen composition spaces; and 3) probabilistic modeling of data in high order composition spaces, which identifies materials most likely to exhibit exceptional properties for any target technology. While these efforts arose from specific research challenges, the methods and concepts are intended to be generally applicable for accelerating scientific discovery via adaption and improvement by the research community.