摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-调查人员讨论人工智能的新发现。根据NewsRx记者从马里兰大学发回的新闻报道，研究表明：“设计具有量身定制的电气和机械性能的超高T导电气凝胶对于各种应用至关重要。”我们的新闻记者引用了Maryl大学的一篇研究报道:“传统的方法依赖于迭代的、耗时的实验,在巨大的参数空间中。本文提出了一种将协作机器人技术与机器学习相结合的集成工作流程,以加速具有可编程性质的连续气凝胶的设计。一台自动移液机器人被设计成264种Ti3C2Tx MXene、纤维素、明胶、明胶等混合物。”通过8个主动学习周期的数据增强,在机器人自动化平台上制作了162个独特的导电气凝胶,并对其进行了表征。建立人工神经网络预测模型。该预测模型执行双向设计任务：(1)根据制造参数预测气凝胶的物理化学性质；(2)根据特定性能要求自动化气凝胶的逆向设计。
Abstract
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."
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