  Although the convergence of high-performance computing, automation, and
machine learning has significantly altered the materials design timeline,
transformative advances in functional materials and acceleration of their
design will require addressing the deficiencies that currently exist in
materials informatics, particularly a lack of standardized experimental data
management. The challenges associated with experimental data management are
especially true for combinatorial materials science, where advancements in
automation of experimental workflows have produced datasets that are often too
large and too complex for human reasoning. The data management challenge is
further compounded by the multi-modal and multi-institutional nature of these
datasets, as they tend to be distributed across multiple institutions and can
vary substantially in format, size, and content. To adequately map a materials
design space from such datasets, an ideal materials data infrastructure would
contain data and metadata describing i) synthesis and processing conditions,
ii) characterization results, and iii) property and performance measurements.
Here, we present a case study for the low-barrier development of such a
dashboard that enables standardized organization, analysis, and visualization
of a large data lake consisting of combinatorial datasets of synthesis and
processing conditions, X-ray diffraction patterns, and materials property
measurements generated at several different institutions. While this dashboard
was developed specifically for data-driven thermoelectric materials discovery,
we envision the adaptation of this prototype to other materials applications,
and, more ambitiously, future integration into an all-encompassing materials
data management infrastructure.
