The fundamentals of XRD spectroscopy and Rietveld refinement can uniquely determine the structure of materials from an XRD pattern. However, the quantity of data and high-throughput methodologies in modern science require innovations to interpret trends and extract patterns from large datasets. In the previous century, the number of known structures and complementary XRD patterns could fit in a few bound volumes. As of today, the Crystallography Open Database 24 The quantity of experimental data is expected to further explode in many emerging highly tunable materials systems. For example, hybrid organic-inorganic semiconductors have highly tunable chemistries and processing parameters that can heavily influence their structure. These highly correlated chemistry-processing-structure-properties relationships have spawned multiple highthroughput studies of the effects of chemistry on structure and resulting electro-optical properties and robotic laboratories that greatly increase productivity and data throughput (Fig ). In fact, the throughput processing of inorganic solids 27 , nanomaterials , polymers , photocatalysts , and more has dramatically increased with robotic and microfluidic synthesis approaches that remove humans from part of the analysis workflow. Such laboratories can synthesize up to hundreds of samples per day and explore the complex space of processing conditions to maximize a desired outcome from two or more processing conditions (Fig ). Continuous compositional libraries can also be created using co-sputtering and evaporation approaches and yield many samples, depending on how the compositional space is probed. (Fig ). Although the existing XRD refinement tools are very robust, the analysis of high-throughput data can take between minutes and hours per XRD pattern depending on complexity and experience, even with the help of database searching and automatic peak-search and refinement algorithms. Therefore, a limitation of (semi-)autonomous materials science labs is not the information that can be extracted from individual XRD patterns for each composition but what structural data and trends can be distilled from thousands of patterns in a timely fashion. Examples from the field of HT experimentation, including a) the methodology of selfdriven laboratories enabling the synthesis and characterization of hundreds of thin films per day , b) a successful optimization of photocatalytic nanoparticles in an autonomous microfluidic system , and c) composition gradient libraries created by use of masks and shutters in the coevaporation of metals; commonly used for many evaporation-processable materials .