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Showing 1–2 of 2 results
Advanced filters: Author: Sara Kadkhodaei Clear advanced filters

  • Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional neural networks.

    • Ali Davariashtiyani
    • Zahra Kadkhodaie
    • Sara Kadkhodaei
    ResearchOpen Access
    Communications Materials
    Volume: 2, P: 1-11

  • Machine learning models can predict the formation energy of compounds with high accuracy and efficiency. Here, the authors develop a deep convolutional network for high-throughput materials screening based on visual image representations of crystals instead of conventional graph structures, providing an alternative state-of-the-art approach that benefits from the most recent advances in image recognition architectures.

    • Ali Davariashtiyani
    • Sara Kadkhodaei
    ResearchOpen Access
    Communications Materials
    Volume: 4, P: 1-12