High Entropy Alloys(HEAs)have drawn great interest due to their exceptional properties compared to conventional materials.The configuration of HEA system is considered a key to their superior properties,but exhausting...High Entropy Alloys(HEAs)have drawn great interest due to their exceptional properties compared to conventional materials.The configuration of HEA system is considered a key to their superior properties,but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging.In this work,we proposed a quantum annealingassisted lattice optimization(QALO)algorithm,which is an active learning framework that integrates the Field-aware Factorization Machine(FFM)as the surrogate model for lattice energy prediction,Quantum Annealing(QA)as an optimizer and Machine Learning Potential(MLP)for ground truth energy calculation.By applying our algorithm to the NbMoTaWalloy,we reproduced the Nb depletion andWenrichment observed in bulk HEA.We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations.Our algorithm highlights the potential of quantum computing in materials design and discovery,laying a foundation for further exploring and optimizing structure-property relationships.展开更多
This paper proposes an event-triggered active disturbance rejection control framework to achieve the simultaneous position and attitude control of a satellite in proximity operations.Firstly,to facilitate the satellit...This paper proposes an event-triggered active disturbance rejection control framework to achieve the simultaneous position and attitude control of a satellite in proximity operations.Firstly,to facilitate the satellite motion description,we derive the relative kinematics and dynamics in terms of dual quaternions with the considerations of internal uncertainties and external disturbances.Then,two kinds of event-triggered mechanisms in the sensor/observer and controller/actuator channels are proposed to reduce the utilization of onboard communication resources and to improve control performance,respectively.The observation error and tracking error of both the attitude and orbit systems are theoretically proven to be asymptotically bounded.Finally,the simulation results show that the proposed method can achieve simultaneous position and attitude tracking between target and chaser satellites with satisfactory control performance and reduced communication rates.展开更多
Solid-state electrolytes(SSEs)can address the safety issue of organic electrolyte in rechargeable lithium batteries.Unfortunately,neither polymer nor ceramic SSEs used alone can meet the demand although great progress...Solid-state electrolytes(SSEs)can address the safety issue of organic electrolyte in rechargeable lithium batteries.Unfortunately,neither polymer nor ceramic SSEs used alone can meet the demand although great progress has been made in the past few years.Composite solid electrolytes(CSEs)composed of flexible polymers and brittle but more conducting ceramics can take advantage of the individual system for solid-state lithium metal batteries(SSLMBs).CSEs can be largely divided into two categories by the mass fraction of the components:“polymer rich”(PR)and“ceramic rich”(CR)systems with different internal structures and electrochemical properties.This review provides a comprehensive and in-depth understanding of recent advances and limitations of both PR and CR electrolytes,with a special focus on the ion conduction path based on polymer-ceramic interaction mechanisms and structural designs of ceramic fillers/frameworks.In addition,it highlights the PR and CR which bring the leverage between the electrochemical property and the mechanical property.Moreover,it further prospects the possible route for future development of CSEs according to their rational design,which is expected to accelerate the practical application of SSLMBs.展开更多
The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized ...The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing,fabrication,and characterization.This review introduces a comprehensive methodology for materials design using cutting-edge quantum computing,with a particular focus on quadratic unconstrained binary optimization(QUBO)and quantum machine learning(QML).We introduce the loop framework for QUBO-empowered materials design,including constructing high-quality datasets that capture critical material properties,employing tailored computational methods for precise material modeling,developing advanced figures of merit to evaluate performance metrics,and utilizing quantum optimization algorithms to discover optimal materials.In addition,we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations.The review also highlights advanced active learning strategies that integrate quantum artificial intelligence,offering a more efficient pathway to explore the vast,complex material design space.Finally,we discuss the key challenges and future opportunities for QML in material design,emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.展开更多
基金supported by the Quantum Computing Based on Quantum Advantage Challenge Research(grant RS-2023-00255442)through the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT).This research also used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DEAC05-00OR22725The authors also would like to thank the Notre Dame Center for Research Computing for supporting all the simulations in this work.Notice:This manuscript has in part been authored by UT-Battelle,LLC under Contract No.DEAC05-00OR22725 with the U.S.Department of EnergyThe United States Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a non-exclusive,paid up,irrevocable,world-wide license to publish or reproduce the published form of the manuscript,or allow others to do so,for U.S.Government 15 purposes.The Department of Energy will provide public access to these results of federally sponsored research in accordance with theDOE Public Access Plan(http://energy.gov/downloads/doe-publicaccess-plan).
文摘High Entropy Alloys(HEAs)have drawn great interest due to their exceptional properties compared to conventional materials.The configuration of HEA system is considered a key to their superior properties,but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging.In this work,we proposed a quantum annealingassisted lattice optimization(QALO)algorithm,which is an active learning framework that integrates the Field-aware Factorization Machine(FFM)as the surrogate model for lattice energy prediction,Quantum Annealing(QA)as an optimizer and Machine Learning Potential(MLP)for ground truth energy calculation.By applying our algorithm to the NbMoTaWalloy,we reproduced the Nb depletion andWenrichment observed in bulk HEA.We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations.Our algorithm highlights the potential of quantum computing in materials design and discovery,laying a foundation for further exploring and optimizing structure-property relationships.
文摘This paper proposes an event-triggered active disturbance rejection control framework to achieve the simultaneous position and attitude control of a satellite in proximity operations.Firstly,to facilitate the satellite motion description,we derive the relative kinematics and dynamics in terms of dual quaternions with the considerations of internal uncertainties and external disturbances.Then,two kinds of event-triggered mechanisms in the sensor/observer and controller/actuator channels are proposed to reduce the utilization of onboard communication resources and to improve control performance,respectively.The observation error and tracking error of both the attitude and orbit systems are theoretically proven to be asymptotically bounded.Finally,the simulation results show that the proposed method can achieve simultaneous position and attitude tracking between target and chaser satellites with satisfactory control performance and reduced communication rates.
基金supported by the National Key R&D Program of China(Grant No.2021YFB2500100)the National Natural Science Foundation of China(Grant Nos.51872196 and 22109114).
文摘Solid-state electrolytes(SSEs)can address the safety issue of organic electrolyte in rechargeable lithium batteries.Unfortunately,neither polymer nor ceramic SSEs used alone can meet the demand although great progress has been made in the past few years.Composite solid electrolytes(CSEs)composed of flexible polymers and brittle but more conducting ceramics can take advantage of the individual system for solid-state lithium metal batteries(SSLMBs).CSEs can be largely divided into two categories by the mass fraction of the components:“polymer rich”(PR)and“ceramic rich”(CR)systems with different internal structures and electrochemical properties.This review provides a comprehensive and in-depth understanding of recent advances and limitations of both PR and CR electrolytes,with a special focus on the ion conduction path based on polymer-ceramic interaction mechanisms and structural designs of ceramic fillers/frameworks.In addition,it highlights the PR and CR which bring the leverage between the electrochemical property and the mechanical property.Moreover,it further prospects the possible route for future development of CSEs according to their rational design,which is expected to accelerate the practical application of SSLMBs.
基金supported by the Shanghai Key Fundamental Research Grant(No.21JC1403300).
文摘The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant strides.However,the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing,fabrication,and characterization.This review introduces a comprehensive methodology for materials design using cutting-edge quantum computing,with a particular focus on quadratic unconstrained binary optimization(QUBO)and quantum machine learning(QML).We introduce the loop framework for QUBO-empowered materials design,including constructing high-quality datasets that capture critical material properties,employing tailored computational methods for precise material modeling,developing advanced figures of merit to evaluate performance metrics,and utilizing quantum optimization algorithms to discover optimal materials.In addition,we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations.The review also highlights advanced active learning strategies that integrate quantum artificial intelligence,offering a more efficient pathway to explore the vast,complex material design space.Finally,we discuss the key challenges and future opportunities for QML in material design,emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.
