Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
This work addresses the problem of efficient and interpretable design of nanoporous materials for sustainable applications, representing an incremental advance with a novel method for a known bottleneck.
The authors tackled the challenge of designing nanoporous materials by developing a method that decomposes structures into local sites for property prediction and interpretation, achieving state-of-the-art accuracy and data efficiency for properties like gas storage and electrical conduction.
Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification. Trained with a constructed database and retrieved datasets, our model achieves state-of-the-art accuracy and data efficiency for property prediction on gas storage, separation, and electrical conduction. Meanwhile, this approach enables the interpretation of the prediction and allows for accurate identification of significant local sites for targeted properties. Through identifying transferable high-performance sites across diverse nanoporous frameworks, our model paves the way for interpretable, symmetry-aware nanoporous materials design, which is extensible to other materials, like molecular crystals and beyond.