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Three separate interlaboratory comparison exercises for environmental contaminants in marine specimens were administered in 2007/2008 by the Chemical Sciences
The Gas Metrology Group periodically participates in bilateral and multi-laboratory international comparisons. During the past two years comparisons have taken
Because of NIST's role as the National Metrology Institute (NMI) in the U.S., the Chemical Sciences Division's measurement capabilities must be benchmarked
Diverse biological sciences, from engineering to epidemiology, benefit from an increased understanding of how genetic code (genotype) determines downstream
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
High precision stable isotope ratio measurements are being used in biological specimens to provide new insights into environmental and climate processes 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
All living things measure their environment, make decisions, and respond. Systematic re-engineering of these living measurement and control systems has the
Solder and solderability are increasingly tenuous links in microelectronics assembly as a consequence of ever shrinking chip and package dimensions, and the
This project will provide data and materials measurements necessary to improve the reliability of solder interconnects degraded by the switch to lead-free
Induced pluripotent stem cell (iPSC) populations are complex, dynamic and heterogeneous. Individual cells within a population are constantly changing, while
Confidence in total or viable microbial cell counting methods is lacking, due in part to a dearth of relevant reference materials. NIST has developed a living
The Cryogenic Technologies Project has been funded by ONR since FY2005 to measure and model the losses in 4 K regenerators and pulse tubes. These thermal
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
By using machine learning approaches, we seek to elucidate underlying universal characteristics of fluids and fluid mixtures that enable property prediction
We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form part of a data
Without the capacity to verify food provenance, the health and well-being of consumers is at risk; moreover, food fraud creates a significant impact on the
We use machine learning and other computational techniques to predict the self-assembly characteristics of multicomponent colloidal materials. This is a
There is always a degree of uncertainty surrounding measurements of polymeric materials, especially complex mixtures with varying distributions in chemistry