To date, the International Zeolite Association Structure Commission (IZA-SC) has cataloged merely 255 distinct zeolite structures, with millions of theoretically possible structures yet to be discovered. The synthesis of a specific zeolite typically necessitates the use of an organic structure-directing agent (OSDA), since the selectivity for a particular zeolite is largely determined by the affinity between the OSDA and the zeolite. Therefore, finding the best affinity OSDA-zeolite pair is the key to the synthesis of targeted zeolite. However, OSDA-zeolite pairs frequently exhibit complex geometric structures, i.e., a complex crystal structure formed by a large number of atoms. Although some existing machine learning methods can represent the periodicity of crystals, they cannot accurately represent crystal structures with local variability. To address this issue, we propose a novel approach called Zeoformer, which can effectively represent coarse-grained crystal periodicity and fine-grained local variability. Zeoformer reconstructs the unit cell centered around each atom and encodes the pairwise distances between this central atom and other atoms within the reconstructed unit cell. The introduction of pairwise distances within the reconstructed unit cell more effectively represents the overall structure of the unit cell and the differences between different unit cells, enabling the model to more accurately and efficiently predict the properties of OSDA-zeolite pairs and general crystal structures. Through comprehensive evaluation, our Zeoformer model demonstrates the best performance on OSDA-zeolite pair datasets and two types of crystal material datasets.

翻译：迄今为止，国际沸石协会结构委员会（IZA-SC）仅收录了255种不同的沸石结构，尚有数百万理论上可能的结构未被发现。特定沸石的合成通常需要使用有机结构导向剂（OSDA），因为对特定沸石的选择性主要由OSDA与沸石之间的亲和力决定。因此，寻找最佳亲和力的OSDA-沸石对是合成目标沸石的关键。然而，OSDA-沸石对常呈现复杂的几何结构，即由大量原子形成的复杂晶体结构。尽管现有的一些机器学习方法能够表示晶体的周期性，但无法准确表征具有局部变异性的晶体结构。为解决这一问题，我们提出了一种名为Zeoformer的新方法，能够有效表示粗粒度的晶体周期性与细粒度的局部变异性。Zeoformer围绕每个原子重构其所在的晶胞，并编码该中心原子与重构晶胞内其他原子之间的成对距离。引入重构晶胞内的成对距离能更有效地表征晶胞的整体结构以及不同晶胞间的差异，使模型能够更准确、高效地预测OSDA-沸石对及一般晶体结构的性质。通过综合评估，我们的Zeoformer模型在OSDA-沸石对数据集及两类晶体材料数据集上均表现出最佳性能。