Zeoformer: Coarse-Grained Periodic Graph Transformer for OSDA-Zeolite Affinity Prediction
<|reference_start|>Zeoformer: Coarse-Grained Periodic Graph Transformer for OSDA-Zeolite Affinity Prediction: 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.<|reference_end|>
@article{shen2024zeoformer:,
    title={Zeoformer: Coarse-Grained Periodic Graph Transformer for OSDA-Zeolite
  Affinity Prediction},
    author={Xiangxiang Shen, Zheng Wan, Lingfeng Wen, Licheng Sun, Ou Yang Ming
  Jie, Xuan Tang, Xian Zeng, Mingsong Chen, Xiao He, Xian Wei},
    journal={arXiv preprint arXiv:2408.12984},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12984},
    primaryClass={cond-mat.mtrl-sci cs.AI}
}