PRISM通过多尺度与周期性建模，提升晶体结构性质预测精度。
PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction
- 设计专家模块分别捕捉晶体的多尺度结构与周期性特征
- 在多个基准上显著优于现有方法，提升晶体性质预测准确率
- 适合材料科学中需精准预测晶体性能的研究者
晶体结构由三维空间中单元晶胞内的重复原子模式构成，给基于图的表征学习带来独特挑战。现有方法常忽略晶体结构固有的周期边界条件和多尺度相互作用。本文提出PRISM，一种图神经网络框架，通过一组专用于编码周期系统不同结构与化学特性的专家模块，显式整合多尺度表示与周期性特征编码。在多个基于晶体结构的基准测试中，实验表明PRISM显著提升了当前最优预测精度，大幅增强晶体性质预测能力。
原文摘要 · Abstract (English)
Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction.
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