High Throughput Materials Science

Machine Learning: Educating the Next Generation Materials Workforce

Ongoing

Annual Bootcamp: Machine Learning for Materials Research (MLMR) The fifth annual MLMR met in the summer of 2020 with 180 attendees from 12 countries, 30% of whom were from industry. Over the 5 years of the bootcamp, we have had attendees from a total of 19 countries. We also run tutorials at MRS

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 the FAIR Digital Object Framework in Material Science

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

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

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

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