SMT-Aided Combinatorial Materials Discovery

In combinatorial materials discovery, one searches for new materials with desirable properties by obtaining measurements on hundreds of samples in a single high-throughput batch experiment. As manual data analysis is becoming more and more impractical, there is a growing need to develop new techniques to automatically analyze and interpret such data. We describe a novel approach to the phase map identification problem where we integrate domain-specific scientific background knowledge about the physical and chemical properties of the materials into an SMT reasoning framework. We evaluate the performance of our method on realistic synthetic measurements, and we show that it provides accurate and physically meaningful interpretations of the data, even in the presence of artificially added noise.