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Article Dans Une Revue Chinese Physics B Année : 2023

Crysformer: An attention-based graph neural network for properties prediction of crystals

Résumé

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory (DFT)-based calculations. Instead, we utilize an attention-based graph neural network that yields high-accuracy predictions. Our approach employs two attention mechanisms that allow for message passing on the crystal graphs, which in turn enable the model to selectively attend to pertinent atoms and their local environments, thereby improving performance. We conduct comprehensive experiments to validate our approach, which demonstrates that our method surpasses existing methods in terms of predictive accuracy. Our results suggest that deep learning, particularly attention-based networks, holds significant promise for predicting crystal material properties, with implications for material discovery and the refined intelligent systems.
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Dates et versions

hal-04429614 , version 1 (31-01-2024)

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Tian 田 Wang 王, Jiahui 家辉 Chen 陈, Jing 婧 Teng 滕, Jingang 金钢 Shi 史, Xinhua 新华 Zeng 曾, et al.. Crysformer: An attention-based graph neural network for properties prediction of crystals. Chinese Physics B, 2023, 32 (9), pp.090703. ⟨10.1088/1674-1056/ace247⟩. ⟨hal-04429614⟩
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