BARCODE: high throughput screening and analysis of soft active materials
Active, responsive, non-equilibrium materials-at the forefront of materials engineering-offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE: Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer, an open-access software that automates high throughput screening of microscopy video data to enable non-equilibrium material optimization and discovery. BARCODE produces a unique fingerprint or 'barcode' of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material-agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate BARCODE's abilities to accelerate and streamline screening and analysis, reveal unexpected correlations and emergence, and enable broad non-expert data access, comparison, and sharing.
Authors
- Ryan McGorty (ORCID: https://orcid.org/0000-0002-6577-060X)
- Ayan Das (ORCID: https://orcid.org/0000-0002-8546-1599)
- Maya Hendija
- Rae M. Robertson‐Anderson (ORCID: https://orcid.org/0000-0003-4475-4667)
- Megan T. Valentine (ORCID: https://orcid.org/0000-0003-4781-8478)
- Qiaopeng Chen
- Keegan Shea Tonry
- Moumita Das
- Aditya Sriram
- Katarina Matic
- Jennifer L. Ross
Institutions
- University of California, Santa Barbara (US)
- Rochester Institute of Technology (US)
- University of San Diego (US)
- Syracuse University (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-12-31
- DOI
- https://doi.org/10.1038/s41467-025-67963-3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00