Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as efficient approximations for large-scale applications, their performance is strongly influenced by the choice of atomic representation. Although modern graph-based approaches have progressively incorporated more structural information, they often fail to capture long-range atomic interactions due to finite receptive fields and local encoding schemes. This limitation leads to distinct crystals being mapped to identical representations, hindering accurate property prediction. To address this, we introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations. To enhance sensitivity to elemental and environmental variations, we employ a data-driven pseudo-particle to generate a synthetic diffraction pattern. PRDNet ensures full invariance to crystallographic symmetries. Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance. The code is openly available at https://github.com/Bin-Cao/PRDNet.

翻译：晶体性质预测受量子力学原理支配，使用传统密度泛函理论精确求解大型多体系统在计算上不可行。虽然机器学习模型已成为大规模应用的高效近似方法，但其性能受原子表示选择的显著影响。尽管现代基于图的方法已逐步纳入更多结构信息，但由于有限的感受野和局部编码方案，它们往往无法捕获长程原子相互作用。这一局限导致不同晶体被映射为相同表示，从而阻碍了准确的性质预测。为解决此问题，我们提出了PRDNet，该模型在图表示之外还利用了独特的倒易空间衍射信息。为增强对元素和环境变化的敏感性，我们采用数据驱动的伪粒子生成合成衍射图案。PRDNet确保完全满足晶体学对称性不变性。我们在Materials Project、JARVIS-DFT和MatBench数据集上进行了大量实验，结果表明所提模型达到了最先进的性能。代码已在https://github.com/Bin-Cao/PRDNet开源。