The Block Copolymer Phase Behavior Database
Open accessAbstract
The Block Copolymer Database (BCDB) is a platform that allows users to search, submit, visualize, benchmark, and download experimental phase measurements and their associated characterization information for di- and multiblock copolymers. To the best of our knowledge, there is no widely accepted data model for publishing experimental and simulation data on block copolymer self-assembly. This proposed data schema with traceable information can accommodate any number of blocks and at the time of publication contains over 5400 block copolymer total melt phase measurements mined from the literature and manually curated and simulation data points of the phase diagram generated from self-consistent field theory that can rapidly be augmented. This database can be accessed via the Community Resource for Innovation in Polymer Technology (CRIPT) web application and the Materials Data Facility. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the SMARTS search syntax (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using Structured Query Language (SQL) and download custom sets of block copolymer data to train machine learning models. Finally, a protocol is presented in which GPT-4, an AI-powered large language model, can be used to rapidly screen and…
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Films offer defined two-dimensional geometries, facilitate surface-sensitive analyses, and are widely used in biomedical applications such as coatings, membranes, or drug delivery systems.
“Films offer defined two-dimensional geometries, facilitate surface-sensitive analyses, and are widely used in biomedical applications such as coatings, membranes, or drug delivery systems.7,30”
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Mandlule, A., Liu, Y., Schwanz, S., Pieper, Y., Scharf, H., Iskhakova, K., Conceição, A. L. C., Wieland, D. C. F., Zeller-Plumhoff, B., Toma, F. M., et al. (2026) · Materials Advances · doi:10.1039/d5ma00886g · Cited by 6 · see citing works
- Authors
- Rebello, N. J., Arora, A., Mochigase, H., Lin, T.-S., Shi, J., Audus, D. J., Muckley, E. S., Osmani, A., & Olsen, B. D.
- Affiliations
- Massachusetts Institute of Technology (US)
- National Institute of Standards and Technology (US)
- Citrine Informatics (United States) (US)
- Corresponding author
- Olsen, Bradley D.
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-08-10
- Volume
- 64
- Issue
- 16
- Pages
- 6464-6476
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4401481890
- Cited by
- 12
- References
- 4
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 1 · 2023: 1
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (2040636)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Block Copolymer Self-Assembly
- Physical Sciences › Chemistry › Organic Chemistry › Advanced Polymer Synthesis and Characterization
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- block copolymers, phase behavior, self-assembly, phase diagram, data schema, SMARTS, self-consistent field theory, machine learning training data, literature screening, GPT-4
- MeSH terms
- Polymers, Phase Transition, Databases, Chemical
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