Materials are technology enablers. Many technologies (e.g., solar fuels, gas steam/turbines, catalysis, additive manufacturing, and corrosion mitigation) await materials solutions via the discovery of optimal new materials and materials processing parameters. Decades of advancements in experimental materials science have resulted in new synthesis methods, faster characterization techniques, and a convergence with computational and data science disciplines. Today, the materials community is fulfilling an international vision for global competitiveness by integrating experiments, computations, theory, and digital data, to realize these technologies. Despite this laudable progress, numerous reports have identified opportunities and gaps at the intersection of high-throughput experimentation, materials data infrastructure, and materials informatics. The High-Throughput Experimental Materials Collaboratory (HTE-MC) aims to specifically target those opportunities and gaps.
The HTE-MC seeks to stimulate U.S. innovation and industrial competitiveness by placing a specific focus on new materials, improved access to experimental data, improved design of cyber-physical systems for experimental materials science and engineering. The HTE-MC seeks to stimulate participation among industry, government, academia, and other public-private consortia. The HTE-MC has a vision of (1) building a sustainable eco system of resources to empower innovators, (2) foster standards and best practices, (3) enable game-changing scientific and technological breakthrough, and (4) recognize intellectual property. The HTE-MC seeks to drive outcomes and impacts such as create a next-generation workforce, facilitate the rapid generation of new experimental data, incentivize open-access practices, and enable the creation of new materials and products.
As envisioned, HTE-MC can be defined as a federated network of integrated materials synthesis‒characterization‒data management services designed to rapidly generate large volumes of high-quality experimental data to populate materials databases and significantly improve predictive design in materials science. We strive to follow a pattern for the data infrastructure model that was successful within the International Virtual Observatory Alliance, but informed by more modern technological practices, such as the FAIR Digital Object Framework. In this model, the sample library would be synthesized at the appropriate member institution, and a new sample library metadata record would be created within a digital object repository. This record would enumerate resolvable persistent identifiers for all data/metadata records (digital objects) associated with the sample library (physical object). Furthermore, A single resolvable persistent identifier would be assigned to the sample library metadata record, which would enable one-step discovery of all data and metadata associated with a sample library. Furthermore, the persistent identifiers associated with library and instruments could be transformed into Quick Response (QR) codes. This would enable the community to develop new tools, which would allow for automated association of the sample library to new measurement data, as the sample is shipped to various instruments within the Collaboratory. We envision an economic model that is similar to the Manufacturing USA Institutes, where small/initial government funding initiates self-sustaining public/private cooperation.