首页|New Findings from University of Maryland in the Area of Machine Learning Describ ed (Machine intelligence accelerated design of conductive MXene aerogels with pr ogrammable properties)
New Findings from University of Maryland in the Area of Machine Learning Describ ed (Machine intelligence accelerated design of conductive MXene aerogels with pr ogrammable properties)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from the Univer sity of Maryland by NewsRx correspondents, research stated, "Designing ultraligh t conductive aerogels with tailored electrical and mechanical properties is crit ical for various applications." Our news journalists obtained a quote from the research from University of Maryl and: "Conventional approaches rely on iterative, time-consuming experiments acro ss a vast parameter space. Herein, an integrated workflow is developed to combin e collaborative robotics with machine learning to accelerate the design of condu ctive aerogels with programmable properties. An automated pipetting robot is ope rated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaral dehyde at different ratios/loadings. After freeze-drying, the aerogels' structur al integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the cons truction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical prop erties from fabrication parameters and (2) automating the inverse design of aero gels for specific property requirements."
University of MarylandEmerging Technol ogiesMachine IntelligenceMachine Learning
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