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{"Abstract":["This is a dataset of 1,000,000 images representing the 17 wallpaper group symmetries. Data is split into testing, training, and validation. This data was used to create a feature extractor for recursive image similarity searching. Additional information will be available in peer-reviewed journals upon publication.  <\/p>"],"Other":["{"references": ["https://imagenet.stanford.edu/"]}"]} 

Mesoporous polyetherimides are important high-performance polymers. Conventional strategies to prepare porous polyetherimides, and polyimide in general, are based on covalent organic framework or thermolysis of sacrificial polymers. The former produces micropores due to intrinsically crosslinked microstructures, and the latter results in macropores because of a blowing effect by the sacrificial polymers. The preparation of mesopores remains a challenge. Here we have prepared mesoporous polyetherimide films by hydrolyzing polylactide- b -polyetherimide- b -polylactide (AIA). Controlled by molecular weight and volume fraction of polylactide in AIA, the porous films exhibit an average pore width of 24 nm. The mesoporous polyetherimide films exhibit a storage modulus of ∼1 GPa at ambient temperatures. This work advances the chemistry of high-performance polymers and provides an alternative strategy to prepare mesoporous polymers, enabling potential use as high-performance membranes for separation, purification, and electrochemistry. 

Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of this interactive tool for researchers to use with their data. 

Abstract Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band‐excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so‐called “better”, “faster”, and “less‐biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long‐short‐term memory (LSTM) β‐variational autoencoders (β‐VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback–Leibler‐divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment‐inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two‐step, three‐state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.