“Discovering Tunable Materials with Unprecedented Properties via High-Throughput Quantum Chemistry”
The solutions to many of society’s most pressing problems rely on the discovery of materials with unprecedented physical and chemical properties that are tailored to an application of interest. Typically, it is not a matter of incremental improvements over existing technologies; rather, there is often an urgent need to identify new kinds of materials altogether. The conventional trial-and-error approach of experimental materials discovery, however, can be extremely time-consuming and may not identify truly top-performing candidates, particularly when they exist beyond the limits of our intuition.
In this talk, I will discuss how quantum chemistry, high-throughput computing, and machine learning can help guide experiments and accelerate the discovery of novel solid-state materials. To demonstrate the impact and versatility of this approach, I will highlight how a novel computational screening platform that I developed can drastically accelerate the discovery of porous framework solids for heterogeneous catalysis, industrial gas separations, and next-generation (opto)electronic devices as it relates to the more sustainable production of valuable chemical products. With advances in data science in mind, I will also discuss my recent work building upon the Materials Project — a publicly accessible database of computed physicochemical properties for over 100,000 solid-state materials — and how such a resource can aid both theorists and experimentalists in the design of alloys and intermetallics with targeted electronic properties. Overall, this work demonstrates how high-throughput virtual screening methods rooted in the fundamental principles of quantum mechanics have set the stage for autonomous materials discovery platforms of the future.