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This talk will explore how artificial intelligence (AI) and advanced computational modeling are driving the next generation of electrochemical systems for chemical manufacturing, offering powerful tools for overcoming significant challenges.
An artificial intelligence model developed to predict the dielectric constants of a wide range of materials, including traditional semiconductors, ferroelectrics, and advanced materials, is introduced, showing high accuracy even for materials that are less studied or for novel materials like graphene and nanowires.
This talk will outline how AI models, when trained on large datasets of experimental electrochemical results, can address the limitations of traditional approaches in autonomous labs, and discuss the potential of this methodology to overcome current limitations in scaling up autonomous laboratories for broader energy t...
This presentation will explore the integration of AI-driven methods and advanced simulation techniques to accelerate the development of high-performance heterogeneous functional materials to enable the development of next-generation devices with high efficiency, scalability, and durability.
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