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At high concentration, long Watson/Crick (WC) double-helixed DNA forms columnar crystal or liquid crystal phases of linear, parallel duplex chains packed on periodic lattices. This can also be a structural motif of short NA oligomers such as the 5’-GTAC-3’ studied here, which makes four-base WC duplexes having hydrophobic blunt ends. End-to-end aggregation then assembles these duplexes into columns and columnar phases are stabilized, in spite of breaks in the double helix every four bases. But the new degrees of freedom introduced by such breaks also enable opportunities for a more diverse palette of self-assembly modes, producing striking self-assemblies of DNA that would not be achievable with contiguous polymers. These include recently reported three-dimensional (3D) periodic low-density nanoscale networks of GCCG, and the twist grain boundary (TGB) phase presented here. In the TGB, columns of GTAC pairs assemble into monolayer sheets in which the duplex columns are mutually parallel. However, unlike in the columnar crystals, these sheets stack in helical fashion into lamellar arrays in which the column axis of each layer is rotated through a 60° angle with respect to the columns in neighboring layers. This assembly of DNA is unique in that it the fills a 3D volume wherein the major grooves of columns in each layer mutually enter and interlock with the major grooves of columns in neighboring layers. This locking is optimized by small adjustments in structure enabled by the breaks in the duplex backbones. 

Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research.