Machine Learnable Language for the Chemical Space of Nanopores Enables Structure–Property Relationships in Nanoporous 2D Materials

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure–property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO₂, N₂, and O₂ gas molecules, enabling structure–property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO₂/N₂, O₂/CO₂, and O₂/N₂ gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.