Chemistry

Perovskite synthesizability using graph neural networks

G. H. Gu, J. Jang, et al.

Discover a groundbreaking graph neural network model developed by Geun Ho Gu and colleagues that accurately predicts the synthesizability of perovskites. This innovative approach sets a new standard with a remarkable true positive rate, surpassing traditional methods and providing a pathway to identifying new material candidates for diverse applications.

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Abstract

This paper proposes a graph neural network model to predict the synthesizability of perovskites, a crucial material in various applications. The model achieves a high out-of-sample true positive rate (0.957), outperforming empirical rule-based methods. Validation shows the model accurately identifies previously synthesized perovskites and excludes those not yet reported. Unlike previous methods limited to metal oxides, this model is generalizable across various perovskite classes. The authors apply the model to identify synthesizable candidates for Li-rich ion conductors and metal halide optical materials.

Publisher

npj Computational Materials

Published On

Jul 20, 2022

Authors

Geun Ho Gu, Jidon Jang, Juhwan Noh, Aron Walsh, Yousung Jung