Autonomous Optimization of Block Copolymer Blend Morphology via Synchrotron X-ray Scattering

The utility of block copolymers (BCPs) in next-generation nanofabrication and optoelectronics is strictly governed by the long-range ordering and precise periodicity of their microphase-separated domains. However, navigating their multidimensional processing space to maximize this structural fidelity is notoriously difficult; conventional optimization relies on Edisonian trial-and-error, which is resource-intensive, slow, and often fails to identify the global optimum. Here, we overcome this bottleneck with an autonomous morphology-discovery workflow that integrates synchrotron grazing-incidence small-angle X-ray scattering (GISAXS), automated scattering data analysis, and Gaussian process-driven Bayesian optimization. Applied to a solvent additive swelling-induced assembly of poly(styrene-b-2-vinylpyridine) (S2VP) copolymer blends, the system autonomously navigates the complex two-dimensional space of blending and solvent uptake to achieve desired polymer ordering. By jointly optimizing for both domain spacing and ordering (via scattering peak sharpness), the autonomous workflow converges on accurate targets using only 20–30 samples—a reduction of over two orders of magnitude compared to the ~3500 samples required for a traditional grid scan of various polymer blends. This work establishes a transferable, high-efficiency autonomous morphology optimization method for accelerating the discovery of highly ordered soft materials.