  The pursuit of more advanced electronics, finding solutions to energy needs,
and tackling a wealth of social issues often hinges upon the discovery and
optimization of new functional materials that enable disruptive technologies or
applications. However, the discovery rate of these materials is alarmingly low.
Much of the information that could drive this rate higher is scattered across
tens of thousands of papers in the extant literature published over several
decades, and almost all of it is not collated and thus cannot be used in its
entirety. Many of these limitations can be circumvented if the experimentalist
has access to systematized collections of prior experimental procedures and
results that can be analyzed and built upon. Here, we investigate the
property-processing relationship during growth of oxide films by pulsed laser
deposition. To do so, we develop an enabling software tool to (1) mine the
literature of relevant papers for synthesis parameters and functional
properties of previously studied materials, (2) enhance the accuracy of this
mining through crowd sourcing approaches, (3) create a searchable repository
that will be a community-wide resource enabling material scientists to leverage
this information, and (4) provide through the Jupyter notebook platform, simple
machine-learning-based analysis to learn the complex interactions between
growth parameters and functional properties (all data and codes available on
https://github.com/ORNL-DataMatls). The results allow visualization of growth
windows, trends and outliers, and which can serve as a template for analyzing
the distribution of growth conditions, provide starting points for related
compounds and act as feedback for first-principles calculations. Such tools
will comprise an integral part of the materials design schema in the coming
decade.
