Graph neural networks (GNNs) inherently excel at representing atomistic material, therefore enabling predictions of properties with high accuracy with embedded descriptors. Approaching to accurate prediction of a variety of physical properties of diverse materials, particularly those with multicomponents, remains challenging due to the complexity of many-body atomic interactions and the reliance on handcrafted symmetry descriptors. Here, we introduce the Wavelet Atomic Neighborhood Network (WANN), a novel framework that implicitly captures many-body interactions through an iterative subembedding module for updating atomic features, thereby eliminating the integration of the predefined feature engineering. A wavelet-based regression component is built up through multiscale feature analysis. WANN demonstrates superior accuracy, achieving small mean absolute error (MAE) in predicting a variety of physical and chemical quantities, including thermodynamical, dielectric, piezoelectric, magnetic, and thermoelectric properties. For instance, it could significantly reduce MAE maximum by 61.64% in comparison to Matformer for shear moduli and by 90.84% in comparison to ALIGNN for the exfoliation energy. This scalable and preprocessing-light deep-learning framework provides an efficient toolkit for facilitating high-throughput screening of emergent materials, like high-entropy alloys and ceramics.
Yang et al. (Thu,) studied this question.
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