Research
Artificial Intelligence in Materials Discovery and Design
As materials scientists and engineers, we work with a design pallet that consists of the periodic table; identifying new materials that enable new functionalities requires a rational approach to screen the almost countless ways the elements can be combined.
Our faculty and students integrate expertise in materials synthesis, characterization, simulation methods, and theory with machine learning to most efficiently explore this vast materials design space. In addition to identifying complex correlations that provide insight into structure-property relationships, artificial intelligence enables us to extrapolate and make predictions about unexplored areas, where existing data is limited. By combining these models with high-throughput synthesis and characterization tools, we can quickly identify new compositions and structures of interest for applications ranging from new catalysts for energy conversion, to new refractory alloys to enable hypersonic flight, to new polymer chemistries for enhanced biosensors.
