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Using methods such as electronic structure (such as density functional theory, tight-binding, quantum Monte Carlo), force-field, machine-learning and quantum
An integrated computational materials engineering (ICME) approach is employed to link experimental and computational models across a variety of time and length
As part of NIST's response to the Materials Genome Initiative (MGI), this project provides resources to address some of the challenges to the wider use of
This project provides and develops novel computational infrastructure (i.e. data archives, open-source codes, APIs) that support the design, execution, and
Our goal is to provide industry with test structures and models of next-generation photovoltaics, with an initial focus on cadmium telluride (CdTe) and CuIn xGa
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
The development of a materials innovation infrastructure (MII) that will enable rapid and significant reductions in the development time for new materials with
Develop a materials innovation infrastructure for the design and discovery of inorganic materials to reduce the time and cost for deploying new materials into
Reproducibility and reuse play fundamental roles in the development of science. With the growth of computational power and tools, simulated experiments have
JARVIS-ML is a repository of machine learning (ML) model parameters, descriptors, and ML related input and target data. JARVIS-ML is a part of the NIST-JARVIS
This project will provide data and materials measurements necessary to improve the reliability of solder interconnects degraded by the switch to lead-free
Using AI tools and physics-, chemistry-, and materials science-based knowledge, this project is aimed at developing machine learning (ML) algorithms to enable
The Materials Genome Initiative will create a new era of materials innovation that will serve as a foundation for strengthening domestic industries in these
For processes involving wetting between two materials, classically defined metrics such as surface or interface energy and contact angle are only a starting
Computational materials design requires a variety of tools to model processing-structure-property relationships across a range of time and length scales. This
MSED is developing methods to statically couple finite element modeling (FEM) to atomistic molecular dynamics (MD) 1. Compared to non-hybrid approaches, this
A comprehensive understanding of how current and next-generation materials impact the performance of semiconductor devices is critical to U.S. Semiconductor
The ubiquity of computers has profoundly influenced science. Sophisticated software tools and easy access to high-performance computing promises to be a
Industry needs thermodynamic and kinetic data for the development of new materials for sustainable energy applications; expanded and new databases are needed