Rethinking Crystal Symmetry Prediction: A Decoupled Perspective

TL;DR

This paper introduces XRDecoupler, a novel framework for crystal symmetry prediction that incorporates chemical intuition and hierarchical learning to improve accuracy, interpretability, and generalization in structural analysis.

Contribution

The paper proposes a decoupled approach with superclass guidance and hierarchical modeling to address sub-property confusion in symmetry prediction.

Findings

Outperforms existing methods on multiple databases

Enhances interpretability of symmetry predictions

Achieves balanced optimization and high-quality representations

Abstract

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.