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


Doerk, G.S.; Stein, A.; Bae, S.; Noack, M.M.; Fukuto, M.; Yager, K.G. "Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends" Science Advances 2023, 9 eadd3687.
doi: 10.1126/sciadv.add3687


We use autonomous x-ray scattering to probe the ordering of block copolymer blends on chemical templates. This autonomous method discovered a variety of new morphologies, including skew, ladder, and alternating motifs.


The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but discovering new morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting due to 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 new morphologies such as aligned alternating subdomains, bilayers, or a novel “ladder” morphology. This work underscores the pivotal role autonomous characterization can play in advancing the paradigm of DSA.