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Data and AI-Driven Materials Science Group

The Data and AI-Driven Materials Science Group develops methods, algorithms, data, and tools, to accelerate the discovery, development, commercialization, and circularity of industrially-relevant materials. We enable the trustworthy use of data and AI-driven methodologies within both experimental and computational materials science and engineering workflows.

Welcome to the Data and AI-Driven Materials Science Group

We develop purposeful solutions to emerging and uniquely-challenging problems at the intersection of materials science and artificial intelligence (AI). We focus on Autonomous and AI-Driven Systems, which requires the development of methods, tools, and platforms to enable accelerated material and product development workflows. Our internally-created autonomous systems enable us to expand our methodology while working on important materials problems, such as Direct Air Capture of Carbon Dioxide and corrosion resistant materials with our Autonomous Scanning Droplet Cell.

Much of our work is focused on developing Autonomous Methods that form the core decision-making capability of self-driving laboratories, as well as robust automated Data and AI-Based Quantitative Analysis. We work on a diverse portfolio of materials characterization techniques, with a particularly strong focus on AI-Based X-Ray and Neutron Scattering Techniques. Similarly, we work on Automated Experimental Technology consisting of robotic and high-throughput experimental infrastructure to enable the rapid synthesis and characterization of materials. As part of our AI-Based Computational Metrology  work, we develop machine learning (ML) algorithms to perform rapid and accurate selection of optimal system features, therefore optimizing performance, as well as material discovery, under a variety of conditions.

Finally, Data and Protocols serve as a foundation and connective tissue for all our efforts. We have aligned our work in support of community adoption of the FAIR Data Principles. Machine actionable data is a critical enabler of data-intensive science and engineering. Within materials science and engineering, process-structure-properties-performance relationships present unique challenges, which have persisted for some time. We have efforts that address materials data interoperability within specific domains such as lithium ion batteries and global interoperability across domains via the emerging FAIR Digital Object Framework.


Core Capabilities

Autonomous and AI-Driven Systems

Development of methods, tools, and platforms to demonstrate and enable accelerated material and product development workflows — Our work in...

Autonomous Methods

Development of methods for autonomous materials synthesis, characterization, and analysis to maximize generation of new knowledge with...

Automated Experimental Technology

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

AI-Based Computational Metrology

Development of machine learning systems to accelerate and scale up physics-based modeling and simulation — Using AI tools and physics-...

Data and Protocols

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

Primary Focus Areas

Climate Mitigation

Global climate change due to rising levels of carbon dioxide in the atmosphere is one of the most significant challenges facing the global...


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


Autonomous Scanning Droplet Cell

We use autonomous experimentation to elucidate the role of composition, processing, and microstructure on the aqueous corrosion of complex metal alloys. To do

EV Battery Passport

Digital battery passports (DBPs), if implemented on a large scale, can provide the electric vehicle battery community with valuable data to support sustainable



News and Updates

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 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


Group Leader

Group Safety Representative