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With increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Communities
Our objective is to evaluate mechanisms of fatigue in bulk silicon, a material that has been traditionally considered to be immune to degradation of mechanical
The goal of this focus area is to leverage the wealth of chemical information present in fingerprint deposits for novel forensic applications. This includes
The goal of this focus area is to enhance the measurement science capabilities and address measurement science challenges present in the analysis of fire debris
The NIST Forensic Chemistry Measurement Program aims to both develop and facilitate the implementation of scientifically valid, robust measurement tools for the
T he goal of this focus area is to improve the field of glass evidence analysis by developing new matrix-matched glass standards and by evaluating more
Nature utilizes a hierarchy of length scales and tiered structure to build lightweight, strong, and tough materials. Engineers look to exploit the advantage of
We develop combinatorial measurement methods and metrologies for the rapid generation of comprehensive and consistent datasets. Manufacturers of devices based
NIST is measuring the trace chemical signatures and quantities of trace residues and background levels that contaminate surfaces associated with the concealment
We develop first-principles-based methods for prediction of atomic arrangements and properties in advanced materials and develop tools for the prediction and
The ability to deposit small amounts of material in a highly controllable and precise fashion is important for creating test materials for trace detection
Compile and evaluate phase equilibrium data for NIST Standard Reference Database 31 (Phase Equilibria Diagrams); disseminate the data in a readily accessible
Ion mobility spectrometry (IMS) is widely used for national security and law enforcement, placed at key transportation checkpoints such as airports and border
Stable and radioactive isotope analysis is an important interdisciplinary tool used by numerous scientists. Therefore, achieving reliable metrology 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
Using AI tools and physics-, chemistry-, and materials science-based knowledge, this project is aimed at developing machine learning (ML) algorithms to enable
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
We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form part of a data
Magnetic sector secondary ion mass spectrometry (SIMS) generates isotopic and elemental information from solid surfaces with depth profiling capabilities to
Penetrating and blunt force phenomena can cause serious trauma. The STG performs research to advance materials science and metrology for materials and equipment
Our goal is to provide analytical tools that allow measurement and prediction of local structure to enable the development of ceramic materials for electronic
The use of standards is considered a critical component in the assurance of analytical quality, allowing reliable measures of detection limits, accuracy, and
All inorganic materials contain point defects in the form of impurities, interstitials, or vacancies. Common imaging techniques fail to capture these defects