Automated phase mapping of high throughput X-ray diffraction data

Combinatorial synthesis and high-throughput characterization have become powerful tools to accelerate the discovery and design of novel materials. However, one key question in the high-throughput workflow is the phase mapping problem. Correct, automatic identification of the number, identity, and fraction of phases in XRD data is a crucial step to perform autonomous high-throughput characterization and establish further understanding on the composition-structure-property relationships. Traditional analysis performed manually could take days for a single material system, while an automatic solver, if lacks the domain-specific knowledge, is likely to give unphysical results. In this work, we demonstrate how we use phase diagrams generated based on DFT calculations databases, such as the Open Quantum Materials Database (OQMD), to provide domain-specific knowledge and enforce reasonable constraints. We show how DFT provides a good initial guess to phase mapping algorithms, we can address the “peak-shifting” caused by alloying behavior using machine learning techniques, and the method is capable of uncovering minor phases present in the XRD data. By combining first-principles calculations and machine learning techniques, our approach enables rapid phase identification and mapping.