The genetic algorithm was used in optimal design of deep jet method pile.The cost of deep jet method pile in one unit area of foundation was taken as the objective function.All the restrains were listed following the ...The genetic algorithm was used in optimal design of deep jet method pile.The cost of deep jet method pile in one unit area of foundation was taken as the objective function.All the restrains were listed following the corresponding specification.Suggestions were proposed and the modified.The real-coded Genetic Algorithm was given to deal with the problems of excessive computational cost and premature convergence.Software system of optimal design of deep jet method pile was developed.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
文摘The genetic algorithm was used in optimal design of deep jet method pile.The cost of deep jet method pile in one unit area of foundation was taken as the objective function.All the restrains were listed following the corresponding specification.Suggestions were proposed and the modified.The real-coded Genetic Algorithm was given to deal with the problems of excessive computational cost and premature convergence.Software system of optimal design of deep jet method pile was developed.
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.
