A large number of 2D materials like graphene can have nanopores—small holes formed by missing atoms through which foreign substances can pass. The properties of these nanopores dictate many of the materials’ properties, enabling the latter to sense gases, filter out seawater, and even help in DNA sequencing.
“The problem is that these 2D materials have a wide distribution of nanopores, both in terms of shape and size,” says Ananth Govind Rajan, Assistant Professor at the Department of Chemical Engineering, Indian Institute of Science (IISc). “You don’t know what is going to form in the material, so it is very difficult to understand what the property of the resulting membrane will be.”
Machine learning models can be a powerful tool to analyze the structure of nanopores in order to uncover tantalizing new properties. But these models struggle to describe what a nanopore looks like.
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