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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
Advanced experimental and theoretical methods are developed to increase the accuracy and precision of X-ray line energies and information derived from X-ray
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
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
We develop first-principles-based methods for prediction of atomic arrangements and properties in advanced materials and develop tools for the prediction and
Our Atomistic Line Graph Neural Network (ALIGNN) models are designed to predict atomistic properties with high accuracy. They can successfully predict single
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
We use machine learning and other computational techniques to predict the self-assembly characteristics of multicomponent colloidal materials. This is a
NIST’s Material Measurement Laboratory and Communications Technology Laboratory are developing a new spectroscopy for intermolecular interactions. The team is
For processes involving wetting between two materials, classically defined metrics such as surface or interface energy and contact angle are only a starting
First responders, athletes, consumers, and the military rely on protective equipment to prevent blunt trauma from an impact. Understanding of the relationship
Computational materials design requires a variety of tools to model processing-structure-property relationships across a range of time and length scales. This
The flow of polymer composites via injection molding, spraying or spreading on surfaces is crucial to polymer composite manufacturing. For single-phase liquids
The Polymer Analytics project was established with the goal of accelerating the discovery of new polymer physics through the development of datasets, methods
The PBD is making new, state-of-the-art measurements of the wavelengths of select atomic X-ray emission lines. These measurements underpin all precision x-ray
High-strength materials are used to protect wearers from injury due to ballistic and stab threats, as well as other potentially life-threatening incidents. The
NIST’s Material Measurement Laboratory and Physical Measurement Laboratory are developing a first-of-kind X-ray testbed for operando monitoring of catalytic