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The ASM Structural Materials Data Demonstration Project SMDDP) is a cooperative research project with the NIST Materials Measurement Laboratory (MML) that is
As part of NIST's response to the Materials Genome Initiative (MGI), this project addresses the need for automation for the assembly and distribution of
We use autonomous experimentation to elucidate the role of composition, processing, and microstructure on the aqueous corrosion of complex metal alloys. To do
We are developing novel machine learning algorithms and incorporating them into closed-loop autonomous systems to accelerate knowledge capture in the lab and in
The Materials Genome Initiative (MGI) creates a new era of materials innovation that will serve as a foundation for strengthening domestic industries in these
An important part of the NIST (National Institute of Science and Technology) MGI (Materials Genome Initiative) effort is to investigate infrastructure and tools
The Biomaterials Group develops advanced measurement capabilities and measurement assurance strategies for characterizing biomaterials, biological systems, and
One of the major goals of the Materials Genome Initiative (MGI) is to facilitate the exchange of materials data to speed material discovery and development. The
DFT is a widely used computational technique in the material science community and it provides interpretative tools for understanding experimental results as
Directed Self Assembly (DSA) is one of the leading candidates for next generation lithography for the semiconductor and data storage industries. DSA combines
With increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Communities
The Materials Data Curation System has the potential to accelerate interoperable data exchange within materials science and engineering. This platform is
Synthetic biomaterials must pass rigorous clinical, toxicological, and regulatory tests before they are introduced into practice. In order to design new
Atomic arrangements underpin the properties of all solids and, as such, are fundamentally important for materials development. The power of computational
The increased performance of modern computational systems has enabled the size and time scales of materials models to be increased significantly. In parallel
We are educating the next generation workforce with MGI skills and providing tools to rapidly deploy these skills. This includes knowledge and tools for machine
Over the last several years machine learning has gained increased attention in the material science community. The ability to perform high throughput in silico
The NIST Materials Data Repository is open to the research community interested in developing best practices in the management of materials data. We are