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However, traditional molecular detection and identification methods like chromatography often involve expensive and bulky equipment and are required to be operated by trained professionals, which severely hinders the further development of small molecule\u2010based applications. Herein, a novel molecular detection platform is introduced by imaging a spatial gradient metasurface consisting of millions of different unique atoms and following with deep learning modeling to classify and quantify small molecules from mixed solutions accurately. The metasurface has a circular gradient geometry, which changes its transmittance intensity pattern based on the surrounding molecules under narrow\u2010band illumination. A convolutional neural network trained on the monochromatic images of the metasurface is employed. The results demonstrate a recognition rate of 96.88% for classification and a mean absolute error of 16.23% for quantification. This novel platform enables label\u2010free, sensitive, and rapid molecular classification and quantification, which opens a new avenue for small molecule classification and quantification and enables possibilities for real\u2010time, on\u2010site, and label\u2010free applications, including environmental monitoring, drug screening, and early diagnosis.<\/jats:p>","DOI":"10.1002\/aisy.202300353","type":"journal-article","created":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T23:34:52Z","timestamp":1698017692000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning\u2010Assisted Molecular Classification and Concentration Prediction by Imaging of a Large\u2010Area Metasurface with Spatially Gradient Geometry"],"prefix":"10.1002","volume":"6","author":[{"given":"Ji","family":"Yang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering Shenzhen Campus of Sun Yat-Sen University  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