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Boosting Crystalline Property Prediction through Dynamical Feature Updating and Wavelet-Denoised Features: A New Deep
Zening Yang1,2, Jin Yu1, Zhengyu Sun1,3
1Jiangsu Province Key Laboratory of Advanced Metallic Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, China.
The Journal of Physical Chemistry Letters
|February 19, 2026
Summary
The Wavelet Atomic Neighborhood Network (WANN) framework accurately predicts material properties by implicitly capturing complex atomic interactions, outperforming existing methods. This deep learning approach accelerates the discovery of novel materials like high-entropy alloys.
Area of Science:
- Computational Materials Science
- Machine Learning in Materials Science
- Deep Learning for Materials Discovery
Background:
- Graph neural networks (GNNs) are effective for material representation but struggle with complex many-body atomic interactions and predefined descriptors.
- Accurate prediction of diverse physical and chemical properties for multicomponent materials remains a significant challenge.
- Existing methods often rely on handcrafted symmetry descriptors, limiting their generalizability and efficiency.
Purpose of the Study:
- To introduce a novel deep learning framework, the Wavelet Atomic Neighborhood Network (WANN), for accurate material property prediction.
- To overcome limitations of existing GNNs by implicitly capturing many-body atomic interactions without predefined feature engineering.
- To enable efficient high-throughput screening of emergent materials.
Main Methods:
- Developed WANN, a novel framework utilizing an iterative subembedding module to update atomic features and implicitly capture many-body interactions.
- Integrated a wavelet-based regression component for multiscale feature analysis.
- Eliminated the need for predefined feature engineering and handcrafted symmetry descriptors.
Main Results:
- WANN demonstrated superior accuracy in predicting various physical and chemical properties, including thermodynamic, dielectric, piezoelectric, magnetic, and thermoelectric properties.
- Achieved significantly reduced mean absolute error (MAE), outperforming Matformer (by 61.64% for shear moduli) and ALIGNN (by 90.84% for exfoliation energy).
- The framework is scalable and requires minimal preprocessing.
Conclusions:
- WANN offers a powerful and efficient deep learning toolkit for materials science research.
- The implicit capture of many-body interactions and multiscale feature analysis contribute to its high accuracy and broad applicability.
- This approach facilitates the accelerated discovery and screening of advanced materials, such as high-entropy alloys and ceramics.
