Scientific data articles within Communications Materials

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  • Review Article
    | Open Access

    Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

    • Patrick Reiser
    • , Marlen Neubert
    •  & Pascal Friederich

  • Article
    | Open Access

    Topological data analysis is an important framework for quantifying the structural and morphological features of soft materials. Here, structural heterogeneity is introduced as a quantitative measure of non-equilibrium mesoscopic order in soft matter and used to track the time-evolution of liquid crystal phase transitions.

    • Ingrid Membrillo Solis
    • , Tetiana Orlova
    •  & Malgosia Kaczmarek

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