Revealing the evolution of order in materials microstructures using multi-modal computer vision
The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La1−xSrxFeO3. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.
Authors
- Christina Doty
- Yingge Du (ORCID: https://orcid.org/0000-0001-9680-1950)
- Steven R. Spurgeon (ORCID: https://orcid.org/0000-0003-1218-839X)
- Sarah Akers
- Khalid Hattar
- Michael Holden (ORCID: https://orcid.org/0000-0001-5181-3501)
- Bethany E. Matthews (ORCID: https://orcid.org/0000-0001-7193-2583)
- R. Comès (ORCID: https://orcid.org/0000-0002-5304-6921)
- Eric Lang (ORCID: https://orcid.org/0000-0001-5136-1875)
- Jenna A. Bilbrey (ORCID: https://orcid.org/0000-0002-4199-7244)
- Rajendra Paudel (ORCID: https://orcid.org/0000-0002-7245-6985)
- Arman Ter-Petrosyan (ORCID: https://orcid.org/0000-0003-0923-8253)
- Le Wang (ORCID: https://orcid.org/0000-0002-7730-9482)
- Kayla Yano
Institutions
- National Laboratory of the Rockies (US)
- Pacific Northwest National Laboratory (US)
- University of New Mexico (US)
- University of Colorado Boulder (US)
- University of California, Irvine (US)
- University of Tennessee at Knoxville (US)
- Auburn University (US)
- University of Delaware (US)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1038/s41524-025-01831-y
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy
- Battelle
- Center for Integrated Nanotechnologies
- Office of Science
- Division of Materials Research
- Biological and Environmental Research
- Laboratory Directed Research and Development
- National Renewable Energy Laboratory
- Sandia National Laboratories
- Los Alamos National Laboratory
- Pacific Northwest National Laboratory