Rethinking Crystal Symmetry Prediction: A Decoupled Perspective
This work addresses the challenge of accurately determining crystal symmetry for materials science, offering a novel approach to improve prediction quality and alignment with chemical intuition.
The paper tackles the problem of sub-property confusion in crystal symmetry prediction by introducing the XRDecoupler framework, which incorporates multidimensional symmetry information as superclass guidance and uses a hierarchical learning model with multi-objective optimization, achieving high performance, interpretability, and generalization across three databases.
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.