利用材料对称性提升沸石吸附预测精度，实现更优泛化能力。
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
- 基于对称性操作改进消息传递机制，增强不同拓扑结构间的参数共享。
- 在插值与外推任务中均准确捕捉二氧化碳吸附趋势，尤其反映铝分布影响。
- 可结合实验数据优化，适用于新型多功纳米材料的逆向设计。
利用深度学习模型精准预测多孔材料中的吸附特性仍具挑战性，尤其在推广至训练数据未涵盖的结构时更为困难。本文提出SymGNN，一种融合材料对称性的图神经网络架构，通过将对称操作引入消息传递机制，提升不同沸石拓扑结构间的参数共享，从而改善模型泛化能力。我们在插值与外推任务上评估了SymGNN，结果表明其能有效捕捉关键吸附趋势，包括骨架结构及铝分布对CO₂吸附的影响。此外，我们采用遗传算法结合模型对实验吸附等温线进行表征，推断可能的铝分布。结果表明，基于模拟数据训练的机器学习模型可有效研究真实材料，并为后续结合实验数据微调及生成式方法实现多功能纳米材料的逆向设计提供了可行路径。
原文摘要 · Abstract (English)
Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task. This challenge becomes even more pronounced when attempting to generalize to structures that were not part of the training data.. In this work, we introduce SymGNN, a graph neural network architecture that leverages material symmetries to improve adsorption property prediction. By incorporating symmetry operations into the message-passing mechanism, our model enhances parameter sharing across different zeolite topologies, leading to improved generalization. We evaluate SymGNN on both interpolation and generalization tasks, demonstrating that it successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO$_2$ adsorption. Furthermore, we apply our model to the characterization of experimental adsorption isotherms, using a genetic algorithm to infer likely aluminium distributions. Our results highlight the effectiveness of machine learning models trained on simulations for studying real materials and suggest promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials.
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