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Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds. 

This paper presents a new approach for predicting thermodynamic properties of perovskites that harnesses deep learning and crystal structure fingerprinting based on Hirshfeld surface analysis. It is demonstrated that convolutional neural network methods capture critical features embedded in two-dimensional Hirshfeld surface fingerprints that enable a quantitative assessment of the formation energy of perovskites. Building on our recent work on lattice parameter prediction from Hirshfeld surface calculations, we show how transfer learning can be used to speed up the training of the neural network, allowing multiple properties to be trained using the same feature extraction layers. We also predict formation energies for various perovskite polymorphs, and our predictions are found to give generally improved performance over a well-established graph network method, but with the methods better suited to different types of datasets. Analysis of the structure types within the dataset reveals the Hirshfeld surface-based method to excel for the less symmetric and similar structures, while the graph network performs better for very symmetric and similar structures. 

Abstract This paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This ‘PFAS-Map’ is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. We provide examples on how the PFAS-Map can be utilized, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources. 

This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich “database” of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites. Our results are compared with other studies on lattice parameter prediction involving both experimental and computationally derived data, and it is shown that our approach is an improvement over other reported methods. The paper concludes by discussing how this work opens new avenues for data-driven high throughput computational predictions of structure–property relationships involving complex crystal chemistries. 

This paper demonstrates the application of Natural Language Processing (NLP) tools to explore large libraries of documents and to correlate heuristic associations between text descriptions in figure captions with interpretations of images and figures. The use of visualization tools based on NLP methods permits one to quickly assess the extent of the research described in the literature related to a specific topic. The authors demonstrate how the use of NLP methods on only the figure captions without having to navigate the entire text of a document can provide an accelerated assessment of the literature in a given domain.