Computational metrology for materials
Name
43578_2025_Article_1651.pdf
Size
1.46 MB
Format
Adobe PDF
Checksum (MD5)
3e64f91d5ccf9d25277b8dcc4164fd84
Author(s)
Warren, James
Read, Jake
Seppala, Jonathan
Strand, Erik
Gershenfeld, Neil
Date Issued
July 31, 2025
Journal
Journal of Materials Research
Publisher
Springer International Publishing
Citation
Warren, J., Read, J., Seppala, J. et al. Computational metrology for materials. J. Mater. Res. 40, 2197–2203 (2025).
Version
Final published version
Abstract
Advanced materials hold great promise, but their adoption is impeded by the challenges of developing, characterizing, and modeling them, then of designing, processing, and producing something with them. Even if the results are open, the means to do each of these steps are typically proprietary and segregated. We show how principles of open-source software and hardware can be used to develop open instrumentation for materials science, so that a measurement can be accompanied by a complete computational description of how to reproduce it. And then we show how this approach can be extended to effectively measure predictive computational models rather than just model parameters. We refer to these interrelated concepts as “computational metrology.” These are illustrated with examples including a 3D printer that can do rheological characterization of unfamiliar and variable materials.
MIT Department
Massachusetts Institute of Technology. Center for Bits and Atoms
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1557/s43578-025-01651-2
