提出解耦框架，解决晶体对称性预测中的混淆问题
Rethinking Crystal Symmetry Prediction: A Decoupled Perspective
- 用多维对称信息作引导，让模型预测更符合化学直觉
- 在三个数据库上准确率显著提升，且泛化能力更强
- 适合需要可解释性与高精度的材料结构分析场景
高效准确地确定晶体对称性是晶体材料结构分析的关键步骤。现有方法通常盲目应用深度学习模型，忽视潜在的化学规律，且实验表明其存在严重的子属性混淆（SPC）问题。为此，本文从解耦视角出发，提出XRDecoupler框架，专门应对SPC问题。仿照化学家的思考过程，创新性地引入多维度晶体对称性信息作为超类指导，确保模型预测过程符合化学直觉。进一步设计分层PXRD模式学习模型和多目标优化方法，实现高质量表征与平衡优化。在三个主流数据库（如CCDC、CoREMOF和InorganicData）上的综合评估表明，XRDecoupler在性能、可解释性和泛化能力方面均表现优异。
原文摘要 · Abstract (English)
Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning models while ignoring the underlying chemical rules. More importantly, experiments show that they face a serious sub-property confusion SPC problem. To address the above challenges, from a decoupled perspective, we introduce the XRDecoupler framework, a problem-solving arsenal specifically designed to tackle the SPC problem. Imitating the thinking process of chemists, we innovatively incorporate multidimensional crystal symmetry information as superclass guidance to ensure that the model's prediction process aligns with chemical intuition. We further design a hierarchical PXRD pattern learning model and a multi-objective optimization approach to achieve high-quality representation and balanced optimization. Comprehensive evaluations on three mainstream databases (e.g., CCDC, CoREMOF, and InorganicData) demonstrate that XRDecoupler excels in performance, interpretability, and generalization.
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