(420d) Automatic Interpretation and Control of X-Ray Diffraction Experiments

The emergence of high-throughput quantum chemical calculations has accelerated the rate at which we can predict new materials for various applications (batteries, solar cells, catalysts, etc.), but the successful synthesis of these materials has often become the slow step in materials design. Autonomous laboratories hold the potential to systematically explore various synthesis routes to new materials, alleviating the painstaking manual trial-and-error approach. However, for an autonomous laboratory to work for inorganic synthesis, we need a method to assess the success of a given synthesis effort without any human intervention. Powder X-ray diffraction (XRD) is the workhorse technique for determining the outcome of materials synthesis. In this talk, I will show how convolutional neural networks can be trained to automatically interpret XRD patterns and identify the phases present in realistic mixtures of crystalline solids. I will also discuss how these same models can be leveraged to adaptively control the XRD experiment itself, improving the quality of predictions and enabling the detection of short-lived intermediates that form during synthesis.