David Elbert
Johns Hopkins University, MD·Materials Science
Materials science now produces huge amounts of data from microscopes, mechanical tests and computer simulations. Artificial intelligence (AI) could help design new materials faster, but only if that data can be found, trusted and reused.
- Fields
Materials Science
Computer Science
Engineering
- Topics
materials science
FAIR data
open science
- Grants
- 2 known grants, through Sep 2029
What they work on
- Coordinate data producers, users, repositories and publishers through the Materials Research Coordination Network (MaRCN) to make depositing materials data in trusted repositories the norm under FAIR (findable, accessible, interoperable, reusable) principles.
- Build community around data and models in electron microscopy and X-ray science, and run workshops and focus groups to train educators and practitioners in FAIR data practices.
- Develop machine-readable context specifications, a five-layer model covering data origins, life-cycle stages, standards and governance rules, so AI agents can act reliably on materials data and models.
- Create a library of reusable, testable AI agent skills for metadata extraction, data format translation, provenance capture and FAIR packaging, plus benchmarks scoring reproducibility in tensile testing, DFT and machine learning case studies.
More about the research
Two linked projects tackle this problem. One builds a national community around FAIR data practices, meaning data that are Findable, Accessible, Interoperable and Reusable. The other lays technical foundations for AI agents that carry out research tasks reliably and reproducibly.
Why it matters
Reliable, reusable materials data and trustworthy AI tools could speed the design of new materials for renewable energy, energy storage, health, quantum computing and advanced manufacturing. Researchers, repositories and industry groups would benefit from shared standards, openly released software and benchmarks, and training that make data reuse and automated analysis more reproducible.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: Disciplinary Improvements: Agentic FAIR: Context, Skills, and Benchmarks for Trustworthy, Reproducible Materials Research
NSF · Oct 2026 – Sep 2029
Collaborative Research: Disciplinary Improvements: Creating a FAIROS Materials Research Coordination Network (MaRCN) in the Materials Research Data Alliance
NSF · Sep 2022 – Aug 2027
Closest labs
Noah Daniels
University of Rhode Island, RI·Computer Science
Develop the MEDFORD language and parser as a human-readable layer that converts researchers' metadata into popular machine-readable metadata standards and FAIR databases.
Kerstin Lehnert
Columbia University, NY·Earth & Environmental Science
Modernize EarthChem, an open geochemistry data repository, by re-engineering its systems for scale and building better human- and machine-readable tools for data discovery, visualization and analysis.
Stuart Chalk
University of North Florida, FL·Computer Science
Develop core components of KnowLedger, an open digital research notebook that records data and notes, connects to online resources, and can be tailored to different scientific workflows.
The school
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
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