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Material Measurement Laboratory /Material Data Division

Data Science and AI Group

The Data Science and AI Group investigates and validates ML models, methods, tools, and platforms applied to complex systems in biology, chemistry, and materials science and engineering.

In infographic highlighting the core features of the Hermes software python package, which are Physics Informed Models, Persistent Storage, and Instrument Data Protocols
Hermes Open-Source Software Supports Autonomous Experimentation in Materials Science and Engineering
A group of workshop participants posing for a group photo with a globe statue and trees in the background.
Workshop Report Published on Autonomous Methodologies for Accelerating X-ray Measurements
Infographic illustrating the 6 approaches discussed in NIST Special Publication on Driving U.S. Innovation in Materials and Manufacturing using AI and Autonomous Labs. The infographic mimics elements of the US Capital building with foundational elements and column elements. Foundation elements include Standards and Testbed Facilities. Column elements include Funding, Public-Private Cooperation, Workforce Development, and End-User Support, which are discussed in the report.
Report Published Outlining Approach to Implement Senate Roadmap for Artificial Intelligence Policy

Welcome to the Data Science and AI Group

We investigate and validate machine learning (ML) models, methods, tools, and platforms applied to complex systems in biology, chemistry, and materials science and engineering; establish accuracy and reliability of ML applied to problems in measurement science and services by developing reference data for stakeholders; and collaborate 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.

Core Capabilities

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Autonomous and AI-Driven Systems

Development of methods, tools, and platforms to demonstrate and enable accelerated material and product development workflows — Our work in...
A flow chart for CAMEO is headed "Decide, Execute, Analyze."

Autonomous Methods

Development of methods for autonomous materials synthesis, characterization, and analysis to maximize generation of new knowledge with...
Diagram illustrating thin film composition spread wafer deposition

Automated Experimental Technology

Development of automated, robotic, and high-throughput experimental infrastructure — Over the past three decades, the materials science and...
High Performance Computing

AI-Based Computational Metrology

Development of machine learning systems to accelerate and scale up physics-based modeling and simulation — Using AI tools and physics-...
Technology Background and Circuit Board With Database Message

Data and Protocols

Development of protocols for interoperable laboratory infrastructure, materials traceability, and FAIR materials data — Machine actionable...

Primary Focus Areas

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Semiconductors

The Material Measurement Science Division has a long history of working with stakeholders in the semiconductor industry to develop new...
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Forensics and Public Health

Forensic science has been a prominent pillar of research at NIST since the release of 2009 National Academies of Sciences report...
A photo of attendees attending MLMR Bootcamp 2018.

Workforce Development

We are educating the next-generation workforce with critical skills and providing tools to rapidly deploy these skills. Traditional...

Projects & Programs

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A Low-Cost Robot Science Kit for Education

Ongoing
Despite its low cost, Legolas has been demonstrated for machine learning-driven hypothesis design, discovery, and validation. For the last four years, Legolas has been used in hands-on courses at the University of Maryland to teach next-generation workforce skills, including ML, control systems...
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Annual Machine Learning for Materials Research Bootcamp

Ongoing
Four days of lectures and hands-on exercises covering a range of data analysis topics from introduction to python and data pre-processing to advanced machine learning analysis techniques. Example topics include: Identifying important features in complex/high dimensional data Visualizing high...
schematic of the scanning droplet cell apparatus

Autonomous Scanning Droplet Cell

Ongoing
Corrosion impacts a broad spectrum of application areas including infrastructure, transportation, and the military. The annual price tag for corrosion mitigation and remediation is 3.4 % of the US GDP. The team is particularly interested in discovering new metallic glasses (metals without long range...

Publications

Awards

Kusne

Aaron Gilad Kusne Named APS Fellow

For pioneering work on applications of machine learning for automated delineation of materials phase diagrams, and for the development of novel physics-informed machine learning for closed-loop autonomous materials exploration and optimization.
Winnie Wong-Ng

Winnie Wong-Ng Inducted into Hall of Distinction

Established in 2004, the Hall of Distinction celebrates individuals who have made significant contributions to science, business, academia, or government as well as to their community. This honor is based on Winnie’s career accomplishments...
Winnie Wong-Ng

ACerS Distinguished Life Member Winnie Wong-Ng

Winnie Wong-Ng is senior research chemist in the Materials Measurement Science Division at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland. She has a Ph.D. in inorganic chemistry from Louisiana State University...
Jason Hattrick-Simpers

Jason Hattrick-Simpers Honored by Maryland Science Center

Dr. Hattrick-Simpers’ pioneering materials science research is aimed at the optimization of properties and processing of novel materials through the integration of machine-learning, artificial intelligence and high-throughput experiment methodologies...

News and Updates

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NIST AI System Discovers New Material

When the words “artificial intelligence” (AI) come to mind, your first thoughts may be of super-smart computers, or robots that perform tasks without needing...
MRS Bulletin

MRS Bulletin Material Matters

The MGI [Materials Genome Initiative] is going to change the way materials science is done. In the next 10-to-20 years, we’ll be doing materials discovery and...

Press Coverage

Contacts

Group Leader

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