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Reconfigurable photonic devices with low optical loss, high\u2010speed switching, and nonvolatile operation are essential especially for photonic neural networks. This study introduces a robust high\u2010throughput methodology that serves as a primary engine for accelerating compositional search within the ternary Ge\u2013Sb\u2013Te (GST) system. Through this approach, we unveiled Ge\n                    <jats:sub>3<\/jats:sub>\n                    Sb\n                    <jats:sub>2<\/jats:sub>\n                    Te\n                    <jats:sub>15<\/jats:sub>\n                    (GST3215), a novel optical phase\u2010change material (OPCM) that mitigates the high optical losses inherent in conventional OPCMs like Ge\n                    <jats:sub>2<\/jats:sub>\n                    Sb\n                    <jats:sub>2<\/jats:sub>\n                    Te\n                    <jats:sub>5<\/jats:sub>\n                    (GST225). With reduced extinction coefficients and an improved figure of merit, GST3215 seamlessly integrates into hybrid silicon micro\u2010ring resonator (MRR), achieving lower insertion loss, a broader modulation range, and precise multilevel optical modulation\u2014ideal traits for reconfigurable photonic systems. Moreover, GST3215 supports programmable nonlinear activation functions (e.g.,\u00a0ReLU, ELU, RBF) in a photonic neural network, lifting classification accuracy on the FashionMNIST dataset from 84.5% to 87.7%. These findings position GST3215 as a game\u2010changer for large\u2010scale photonic integration and optical computing.\n                  <\/jats:p>","DOI":"10.1002\/nap2.70212","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:52:20Z","timestamp":1784091140000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reconfigurable Nonlinear Activation Using Low\u2010Loss Ge\n                    <sub>3<\/sub>\n                    Sb\n                    <sub>2<\/sub>\n                    Te\n                    <sub>15<\/sub>\n                    for Photonic Neural Networks"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3431-0590","authenticated-orcid":false,"given":"Yida","family":"Dong","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology  Shenzhen 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