Researchers from the Indian Institute of Science have developed a novel approach called STRONG (STring Representation Of Nanopore Geometry) to predict and analyze nanopore properties in 2D materials like graphene. This innovative language encodes the structure and shape of nanopores, using sequences of characters that represent the atomic configurations along the nanopore’s edges. STRONG facilitates machine learning models in recognizing and predicting nanopore properties, streamlining data analysis by identifying functionally equivalent nanopores. By training neural networks on these encoded sequences, researchers can predict material properties and explore reverse engineering, such as designing nanopores with desired functionalities for applications like gas separation, water desalination, and DNA sequencing. The study also introduces the potential for creating digital twins of materials, allowing enhanced prediction and innovative use of materials based on their nanoporous structures. This advancement holds promise for tackling challenges like carbon emissions reduction and efficient resource utilization.
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