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Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures



Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time-consuming and labour-intensive experiments and simulations.



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