High Throughput Materials Science

high quality rendering of a pipetting robot connected to a neutron scattering instrument

Autonomous Formulation Lab

Ongoing

The Autonomous Formulation Laboratory is a joint program between the nMat consortium, NCNR, and NIST's Material Measurement Lab focused on accelerating materials discovery and formulation optimization through artificial intelligence (AI) and machine learning (ML) directed, multimodal scattering

Sustainable Metals Processing and Alloy Development

Ongoing

Foundational measurement science is being developed to support sustainable innovations through alloy design and optimization. The integration of experiments and computational thermodynamics and kinetics tools with mechanistic models enables the prediction of key processing-structure-property

Autonomous Scanning Droplet Cell

Ongoing

Corrosion impacts a broad spectrum of application areas including infrastructure, transportation, and the military. The annual price tag for corrosion mitigation and remediation is 3.4 % of the US GDP. The team is particularly interested in discovering new metallic glasses (metals without long range

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

Facilitating the Adoption of FAIR Digital Objects in Materials Science and Engineering

Ongoing

The FAIR Data Principles define a set of aspirational guidelines for those whose goal is to improve the reusability of their data. However, the FAIR Data Principles do not define an explicit set of requirements to achieve FAIRness, which may vary within and across domains. Various projects have

Atomistic tools for structure-property investigations

Ongoing

Interatomic Potential Repository The Interatomic Potentials Repository (IPR) provides a source for interatomic potentials (force fields), related files, and evaluation tools to help researchers obtain interatomic models and judge their quality and applicability. The files provided are of known

Advanced Materials Design: Electronic and Functional Applications

Ongoing

Accelerating Materials Discovery using Machine Learning and AI Using machine learning and AI techniques along with high-throughput DFT calculations materials with specific properties are identified to accelerate the the discovery process for a variety of applications. Some of the specific materials

Lattice constant for AI using various exchange-correlation functionals

DFT Benchmarking

Ongoing

Since its inception exactly 50 years ago this year, density-functional theory (DFT) has evolved from an exotic idea of physicists into the most-widely used tool for the computational prediction of materials’ structures and electronic, optical, magnetic, and mechanical properties. While DFT presents

Searching the ICSD for new functional materials from first principles

Ongoing

The combination of increased computing power and the improvement and streamlining of numerical techniques over the past decade have allowed for high quality first principles density functional theory calculations to be routinely performed on large numbers of compounds. This computational power, when

JARVIS-FF

Ongoing

Many classical force-fields are developed for a particular set of properties (such as energies) and they may not have been tested for properties or configurations outside the training (such as elastic constants, defect formation energies or energies for metastable phases). JARVIS-FF provides an

JARVIS-DFT

Ongoing

JARVIS-DFT hosts materials property data for ~40000 bulk and ~1000 low-dimensional crystalline materials and the database is continuously expanding. Some of the properties in the database are: formation energies, bandgaps, elastic, piezoelectric, dielectric constants, and magnetic moments

NIST Materials-focused Empirical Potentials Repositories and Efforts

Ongoing

SOFT MATTER WebFF: Force-field repository for organic and soft materials Frederick R. Phelan Jr., 1 and Huai Sun 2 1Materials Science and Engineering Division, NIST, Gaithersburg, MD 20899 2Aeon Technology Inc. and School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University