用可解释的神经网络设计多孔材料，高效发现高性能新结构。
Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
- 基于三维周期空间采样，分解局部几何环境进行属性预测与贡献分析。
- 在气体存储、分离和电子性质预测上达到顶尖精度与数据效率。
- 揭示可迁移的关键位点，支持逆向设计高氮气吸附与分离性能材料。
多孔框架材料在可持续应用中潜力巨大，但其庞大的化学空间限制了高效系统的结构设计。尽管机器学习为加速探索提供了可能，现有方法往往缺乏对晶体几何与宏观性能之间关联的可解释性或保真度。本文提出一种基于三维周期空间采样的站点解析等变学习框架，将多孔结构分解为局部几何环境，实现属性联合预测与逐位贡献分析。模型在构建与检索数据集组合上训练，在气体存储、分离及电子性质预测任务中均达到当前最优性能，且具有优异的数据效率。关键的是，该框架揭示了跨不同框架的可解释局部结构-性能关系，识别出具有高贡献度的通用结构基元。基于这些学习到的结构特征，我们进一步实现了金属有机框架材料的逆向设计，所得新材料在氮气吸附量上创纪录，具备强二氧化碳/氮气分离能力，并接近零电子带隙，经物理模拟验证有效。
原文摘要 · Abstract (English)
Reticular frameworks hold promise for diverse sustainable applications, yet their immense chemical space limits efficient and systematic design. While machine learning provides a compelling pathway to accelerate exploration, existing approaches often lack either interpretability or fidelity in linking crystal geometry to emergent properties. Here, we introduce a site-resolved equivariant learning framework based on three-dimensional periodic space sampling, which decomposes reticular structures into local geometric environments for simultaneous property prediction and site-wise contribution analysis. Trained on a combination of constructed and retrieved datasets, the model achieves state-of-the-art accuracy and data efficiency across gas storage, gas separation, and electronic-property prediction tasks. Importantly, the framework reveals interpretable local structure-property relationships by identifying transferable high-contribution sites across diverse frameworks. Leveraging these learned motifs, we further demonstrate inverse design of new metal-organic frameworks exhibiting record-high N2 storage, strong CO2/N2 separation performance, and near-zero electronic band gaps, validated by physics-based simulations.
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