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Bimodal networked DEs have emerged as a class of high\u2010performance DEs owing to their tunable mechanical properties and electromechanical instability (EMI) suppression capability. However, their development is limited by complex synthesis and empirical trial\u2010and\u2010error methods. Here, an AI\u2010assisted design framework is presented that integrates experimental data with machine learning models and demonstrates it on the design of a typical bimodal networked DE material, processible high\u2010performance dielectric elastomer (PHDE). The neural network in the framework enabled accurate prediction of mechanical properties, while the support vector machine reliably classified EMI behavior. The framework also achieved reverse design of DEs from user\u2010defined targets in which experimentally validated formulations matched predictions and demonstrated EMI suppression. A soft gripper fabricated from the optimized PHDE materials lifted 25 times its own weight, demonstrating the practical utility of the framework. This study establishes a data\u2010efficient and scalable approach for designing advanced DE\u2010based artificial muscles, accelerating discovery while reducing experimental workload.<\/jats:p>","DOI":"10.1002\/aisy.202500917","type":"journal-article","created":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T21:39:56Z","timestamp":1759700396000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data\u2010Driven Design of Bimodal Networked Dielectric Elastomers for High\u2010Performance Artificial Muscles"],"prefix":"10.1002","volume":"8","author":[{"given":"Ofoq","family":"Normahmedov","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering Zhejiang University  Hangzhou 310000 China"},{"name":"ZJU\u2010UIUC Institute Zhejiang University  Haining 314400 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