  The current bulk materials discovery cycle has several inefficiencies from
initial computational predictions through fabrication and analyses. Materials
are generally evaluated in a singular fashion, relying largely on human-driven
compositional choices and analysis of the volumes of generated data, thus also
slowing validation of computational models. To overcome these limitations, we
developed a high-throughput rapid experimental alloy development (HT-READ)
methodology that comprises an integrated, closed-loop material screening
process inspired by broad chemical assays and modern innovations in automation.
Our method is a general framework unifying computational identification of
ideal candidate materials, fabrication of sample libraries in a configuration
amenable to multiple tests and processing routes, and analysis of the candidate
materials in a high-throughput fashion. An artificial intelligence agent is
used to find connections between compositions and material properties. New
experimental data can be leveraged in subsequent iterations or new design
objectives. The sample libraries are assigned unique identifiers and stored to
make data and samples persistent, thus preventing institutional knowledge loss.
