Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends

• Published in: Science advances Vol. 9; no. 2; p. eadd3687
• Main Authors: Doerk, Gregory S, Stein, Aaron, Bae, Suwon, Noack, Marcus M, Fukuto, Masafumi, Yager, Kevin G
• Format: Journal Article
• Language: English
• Published: United States AAAS 13.01.2023; American Association for the Advancement of Science
• Subjects: Materials Science; NANOSCIENCE AND NANOTECHNOLOGY; Physical and Materials Sciences; Research Methods; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.add3687

The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA.