UQ

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

Stochastic Network Growth Simulation for Photopolymerization

Photopolymerization, particularly in crosslinked systems, proceeds via a complex reaction-diffusion process. Initiation, propagation (including those leading to

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

Integrated Nanoscale Experimentation, Prediction, and Validation

The increased performance of modern computational systems has enabled the size and time scales of materials models to be increased significantly. In parallel

Uncertainty Quantification in Computational Materials Science

We are investigating and promoting applications of uncertainty quantification to problems in computational material science. Detailed investigation of promising

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

High-Precision Structural Measurements

Atomic arrangements underpin the properties of all solids and, as such, are fundamentally important for materials development. The power of computational

CHiMaD Phase Field Community

CHiMaD Phase Field Website Repository See the live website at pages.nist.gov/chimad-phase-field. In January 2015 a group of phase field

Statistical methods for MGI metrology

We are developing statistical methods to enable measurement science and metrology for MGI (project started in 2011).

Statistical modeling of images of electronic materials

To develop components of electrical devices at the scale of a few atoms for supercomputer, communications and health care technologies, a quantitative

Assessment of Uncertainty in CALPHAD Descriptions

This project addresses the development of methods for uncertainty quantification for CALPHAD descriptions.