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Abstract Art and materials innovation have always been intertwined, dating back to the earliest human creations. In modern times, however, the increasing specialization of materials science often restricts artists' access to cutting‐edge materials. Here, the materials science aspects of an art‐science collaboration between artist Kimsooja and the Wiesner Lab at Cornell University, are detailed. The project involves the development of a custom‐made iridescent block copolymer coating by means of self‐assembly, originally applied to transparent window panels of a façade for the ≈14 m tall art installation:A Needle Woman: Galaxy Is a Memory, Earth is a Souvenirby artist Kimsooja. After several exhibitions in the US and Europe, the installation is now part of the permanent museum collection at Yorkshire Sculpture Park in Wakefield, UK. Full characterization of the solution blade‐cast coatings show shear aligned, standing up lamellar morphologies that behave as volume‐phase gratings with periodicities between 300 and 400 nm. Coatings are also applied to foldable (origami) paper and converted into iridescent porous ceramic materials. It is hoped this work inspires and informs communities across materials science, the arts, and architecture. 

Crystalline and 3D continuous mesoporous quaternary CsTaWO 6 semiconductors are prepared with different degrees of long‐range periodic order and local order, respectively, to investigate the influence of periodic pore order on the photocatalytic performance in hydrogen evolution of mesoporous photocatalysts. The degree of long‐range order of the mesopores is changed by modifying the ratio between metal precursors and soft polymer template poly(isoprene‐ b ‐styrene‐ b ‐ethylene oxide) (PI‐ b ‐PS‐ b ‐PEO; ISO) in the sol–gel synthesis. Long‐range periodic order is found to have no appreciable advantage compared with an only locally ordered continuous pore system. On the contrary, nonperiodically ordered mesopores result in higher activity toward photocatalytic hydrogen evolution, even with slightly smaller pore diameter and lower cumulative pore volume. Most importantly, it is shown that pore connectivity and heterogeneous pore systems in mesoporous photocatalysts play a major role for hydrogen evolution when other parameters are confirmed to be not rate limiting. 

Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework – calledCyRSoXS(https://github.com/usnistgov/cyrsoxs) – is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument foroperandoanalytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposingCyRSoXSto Python usingPybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.