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Material Measurement Laboratory /Material Data Division

Data Science and AI Group

Investigating and validating AI/ML models, methods, tools, and platforms applied to complex systems in biology, chemistry, and materials science and engineering.

The Data Science and AI Group is part of the Material Data Division at NIST. The group:

  • Investigates and validates AI/ML models, methods, tools, and platforms applied to complex systems in biology, chemistry, and materials science and engineering.
  • Establishes accuracy and reliability of AI/ML applied to problems in measurement science and services by developing reference data for stakeholders.
  • Collaborates with bench scientists on a variety of projects related to (1) analyzing measurements of complex biological, chemical, and materials systems, (2) modeling the behavior of these complex systems using data-driven approaches, and (3) collaborating to develop methods and standards necessary for validated autonomous laboratory technologies.

Projects & Programs

component 1

Consistency Analysis and Uncertainty in ‘omic Data

Ongoing
‘Omics is increasingly moving out of the laboratory and towards use in industrial and commercial applications. For instance, in biomanufacturing, there is a need for quality control when developing biotherapeutics, which will almost certainly require a machine-learning classifier to separate...
MLF_EquationofState1

Machine Learning Fluid Equations of State

Ongoing
Understanding the thermodynamic properties of fluids and fluid mixtures is of central importance in many fields of science and engineering ranging from medicine to consumer products. The nature of the particles in a fluid can vary greatly depending on the type of interactions present, e.g., dipole...
Illustration with logo style printing: “organic ingredients”, “100% natural”, “100% organic”; “natural ingredients”, and “authentic” centered in bold letters.

Machine Learning to Predict Food Provenance

Ongoing
Adulteration of food and food products is a pernicious problem which is difficult to solve as supply chains and international trade routes become increasingly complex; yet agriculture contributed over $1 trillion to the US GDP in 2017, [1] illustrating the importance of protecting this and related...

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