High Throughput Materials Science

Machine Learning: Educating the Next Generation Materials Workforce

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

Autonomous Scanning Droplet Cell

We use autonomous experimentation to elucidate the role of composition, processing, and microstructure on the aqueous corrosion of complex metal alloys. To do

A digital object is composed of Data, Metadata, Protocols & Operations, and Persistent Identifiers.

Facilitating the Adoption of the FAIR Digital Object Framework in Material Science

With increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Communities

Advanced Materials Design: Electronic and Functional Applications

Using methods such as electronic structure (such as density functional theory, tight-binding, quantum Monte Carlo), force-field, machine-learning and quantum

NIST Materials-focused Empirical Potentials Repositories and Efforts

Because classical empirical potentials/force-fields are widely used to model material behavior via Molecular Dynamics or Monte Carlo simulations, NIST

Lattice constant for AI using various exchange-correlation functionals

DFT Benchmarking

DFT is a widely used computational technique in the material science community and it provides interpretative tools for understanding experimental results as

Searching the ICSD for new functional materials from first principles

The Inorganic Crystal Structure Database (ICSD, SRD 3 ), maintained at NIST, contains over 100,000 crystal structures which are known to exist and could

JARVIS-FF

JARVIS-FF (Website: https://www.ctcms.nist.gov/~knc6/periodic.html ) is a repository of classical force-field/potential calculation data to help users to select

JARVIS-DFT

JARVIS-DFT (Website: https://www.ctcms.nist.gov/~knc6/JVASP.html ) is a materials property repository focused on density functional theory (DFT) predictions of