The energy industry,now in an era of digitization driven by computational design,is gradually moving towards automating the entire process from computational prediction to device assembly,aiming to minimize the relian...The energy industry,now in an era of digitization driven by computational design,is gradually moving towards automating the entire process from computational prediction to device assembly,aiming to minimize the reliance on time-consuming,manual trial-and-error validation.In this study,guided by computational density functional theory(DFT)predictions,a humanoid robotic arm,based on artificial intelligence(AI),was creatively utilized to assemble clean energy devices,solid oxide fuel cells(SOFCs).The material La_(0.35)Bi_(0.15)Sr_(0.5)FeO_(3-δ)(LBSF)was DFT-predicted to have high oxygen reduction reactions(ORRs)ability,suitable for the cathode in SOFCs compared to the conventional La_(0.5)Sr_(0.5)FeO_(3-δ)(LSF).The material was made into ink then passed to the assembly platform with AI-driven robotics.AI-driven robotics was employed with an imitation learning method to effectively learn skills directly from human demonstrations,thereby alleviating researchers from labor-intensive tasks.We demonstrate our approach for autonomous SOFCs fabrication.For easy platform usage in the future,Large Language Models(LLMs)were incorporated to understand human commands.Visual information was captured by an RGBD camera to identify and locate the cathode painting spot.An imitation learning framework was then applied to learn the painting path from human operations and can be generalized to different conditions.The auto-fabricated single cells with the DFT-predicted LBSF cathode were tested and achieved a power density of 966mW∕cm^(2)at 700℃,more than double the performance of LSF.By integrating computational design with an AI-driven assembly platform,this study marks an initial step towards an AI-driven material lab,exponentially accelerating material design in the near future.The platform can also help disabled researchers achieve their ideas through the behavior cloning approach.展开更多
Subject Code:C05 With the support by the National Natural Science Foundation of China,the research team led by Dr.Li Qing(李晴)at the State Key Laboratory of Protein and Plant Gene Research,School of Life Sciences and...Subject Code:C05 With the support by the National Natural Science Foundation of China,the research team led by Dr.Li Qing(李晴)at the State Key Laboratory of Protein and Plant Gene Research,School of Life Sciences and Peking-Tsinghua Center for Life Sciences,Peking University,Beijing,recently reported that展开更多
The disturbance torque generated via solar array drive assembly(SADA) can significantly degrade the key performance of satellite.The discussed SADA is composed of a two-phase hybrid stepping motor and a set of two-sta...The disturbance torque generated via solar array drive assembly(SADA) can significantly degrade the key performance of satellite.The discussed SADA is composed of a two-phase hybrid stepping motor and a set of two-stage straight gear reducer. Firstly, the vibration equation of the two-phase hybrid stepping motor is established via simplifying and linearizing the electromagnetic torque.Secondly, based on the vibration equation established, the disturbance torque model of SADA is created via force analysis and force system simplification. Thirdly, for precisely ground measuring the disturbance torque aroused by SADA, a measurement system,including a strain micro-vibrations measurement platform(SMMP) and a set of gravity unloading device(GUD), is designed.Fourthly, the proposed disturbance torque model is validated by measuring and simulating the disturbance torque produced via SADA driving rigid load through GUD. The results indicate that, the proposed disturbance torque model holds the ability to describe the disturbance torque caused by SADA with high precision. Finally, the disturbance torque emitted by SADA driving a flexible load, designed to simulate solar array, is modeled and simulated via using fixed-interface mode synthesis method(FIMSM). All the conclusions drawn from this article do have a meaningful help for studying the disturbance torque produced by SADA driving solar array on orbit.展开更多
文摘The energy industry,now in an era of digitization driven by computational design,is gradually moving towards automating the entire process from computational prediction to device assembly,aiming to minimize the reliance on time-consuming,manual trial-and-error validation.In this study,guided by computational density functional theory(DFT)predictions,a humanoid robotic arm,based on artificial intelligence(AI),was creatively utilized to assemble clean energy devices,solid oxide fuel cells(SOFCs).The material La_(0.35)Bi_(0.15)Sr_(0.5)FeO_(3-δ)(LBSF)was DFT-predicted to have high oxygen reduction reactions(ORRs)ability,suitable for the cathode in SOFCs compared to the conventional La_(0.5)Sr_(0.5)FeO_(3-δ)(LSF).The material was made into ink then passed to the assembly platform with AI-driven robotics.AI-driven robotics was employed with an imitation learning method to effectively learn skills directly from human demonstrations,thereby alleviating researchers from labor-intensive tasks.We demonstrate our approach for autonomous SOFCs fabrication.For easy platform usage in the future,Large Language Models(LLMs)were incorporated to understand human commands.Visual information was captured by an RGBD camera to identify and locate the cathode painting spot.An imitation learning framework was then applied to learn the painting path from human operations and can be generalized to different conditions.The auto-fabricated single cells with the DFT-predicted LBSF cathode were tested and achieved a power density of 966mW∕cm^(2)at 700℃,more than double the performance of LSF.By integrating computational design with an AI-driven assembly platform,this study marks an initial step towards an AI-driven material lab,exponentially accelerating material design in the near future.The platform can also help disabled researchers achieve their ideas through the behavior cloning approach.
文摘Subject Code:C05 With the support by the National Natural Science Foundation of China,the research team led by Dr.Li Qing(李晴)at the State Key Laboratory of Protein and Plant Gene Research,School of Life Sciences and Peking-Tsinghua Center for Life Sciences,Peking University,Beijing,recently reported that
文摘The disturbance torque generated via solar array drive assembly(SADA) can significantly degrade the key performance of satellite.The discussed SADA is composed of a two-phase hybrid stepping motor and a set of two-stage straight gear reducer. Firstly, the vibration equation of the two-phase hybrid stepping motor is established via simplifying and linearizing the electromagnetic torque.Secondly, based on the vibration equation established, the disturbance torque model of SADA is created via force analysis and force system simplification. Thirdly, for precisely ground measuring the disturbance torque aroused by SADA, a measurement system,including a strain micro-vibrations measurement platform(SMMP) and a set of gravity unloading device(GUD), is designed.Fourthly, the proposed disturbance torque model is validated by measuring and simulating the disturbance torque produced via SADA driving rigid load through GUD. The results indicate that, the proposed disturbance torque model holds the ability to describe the disturbance torque caused by SADA with high precision. Finally, the disturbance torque emitted by SADA driving a flexible load, designed to simulate solar array, is modeled and simulated via using fixed-interface mode synthesis method(FIMSM). All the conclusions drawn from this article do have a meaningful help for studying the disturbance torque produced by SADA driving solar array on orbit.
