首页|Carnegie Mellon University Researchers Provide Details of New Studies and Findin gs in the Area of Robotics (Differentiable modeling and optimization of non-aque ous Li-based battery electrolyte solutions using geometric deep learning)
Carnegie Mellon University Researchers Provide Details of New Studies and Findin gs in the Area of Robotics (Differentiable modeling and optimization of non-aque ous Li-based battery electrolyte solutions using geometric deep learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on robotics have been pr esented. According to news originating from Carnegie Mellon University by NewsRx correspondents, research stated, “Electrolytes play a critical role in designin g next-generation battery systems, by allowing efficient ion transfer, preventin g charge transfer, and stabilizing electrode-electrolyte interfaces.” Funders for this research include Doe | Advanced Research Projects Agency - Ener gy. The news journalists obtained a quote from the research from Carnegie Mellon Uni versity: “In this work, we develop a differentiable geometric deep learning (GDL ) model for chemical mixtures, DiffMix, which is applied in guiding robotic expe rimentation and optimization towards fast-charging battery electrolytes. In part icular, we extend mixture thermodynamic and transport laws by creating GDL-learn able physical coefficients. We evaluate our model with mixture thermodynamics an d ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electroly tes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients.”
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