This paper characterizes the boundedness and compactness of weighted composition operators between Bers-type space （or little Bers-type space） and Bergman space. Some estimates for the norm of weighted composition o...This paper characterizes the boundedness and compactness of weighted composition operators between Bers-type space （or little Bers-type space） and Bergman space. Some estimates for the norm of weighted composition operators between those spaces are obtained.展开更多
In this paper, using incremental equilibrium equation, the authors have studiedthe effeet of ultimate bearing capacity of every component on structuralstability, and discussed the stability analysis method for space c...In this paper, using incremental equilibrium equation, the authors have studiedthe effeet of ultimate bearing capacity of every component on structuralstability, and discussed the stability analysis method for space compositestructures. With the help of the test results for the concrete filled ateel tubeskeleton of the long-spen RC arch bridse, it is proved that the proposed methodis accurate and reliable.展开更多
Wepropose machine learning(ML)models to predict the electron density—the fundamental unknown of a material’s ground state—across the composition space of concentrated alloys.From this,other physical properties can ...Wepropose machine learning(ML)models to predict the electron density—the fundamental unknown of a material’s ground state—across the composition space of concentrated alloys.From this,other physical properties can be inferred,enabling accelerated exploration.A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species.To address this,we employ Bayesian Active Learning(AL),which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks.Compared to the strategic tessellation of the composition space,Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary(SiGeSn)and 1.7 for quaternary(CrFeCoNi)systems.We also introduce easy-to-optimize,body-attached-frame descriptors,which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases.Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.展开更多
The study examined joint discontinuity spacing effect on drilling condition and performance in selected rocks in llorin and Ibese areas, Nigeria. Five samples for each rock type （gneiss and limestone） were tested in...The study examined joint discontinuity spacing effect on drilling condition and performance in selected rocks in llorin and Ibese areas, Nigeria. Five samples for each rock type （gneiss and limestone） were tested in the laboratory for chemical, physical and mechanical properties. Dip direction and joint spacing were measured using compass clinometers. The chemical composition was determined using X-ray Fluores- cence （XRF） spectrometer. The results show that gneiss has SiO2 of 61.88g and limestone has CaO content of 52.3g. The average dry density of gneiss and limestone are 2.6 and 2.39 g/cm^3, respectively. The uniaxial compressive strength of gneiss and limestone are 195 and 93.83 MPa, respectively. These rocks are classified as strong and moderately strong rock. Gneiss and limestone have mean joint discontinuity spacing of 0.79 and 0.25 m, which classified them as moderate and wide joint spacing respectively. Joint spacing was correlated with specific energy, bit wear and uniaxial compressive using Statistical Package for Social Science （SPSS）. The regression model has multiple coefficient of correlation of R^2 = 0.791 and R^2 =0.995 for gneiss and limestone, respectively. The variation in joint spacing could be attributed to spe- cific energy, bit wear and uniaxial compressive strength which affect drilling condition and performance. Ultimately, as joint spacing gets closer, the drilling velocity increases, drill string will be stable.展开更多
he expressions of the composition space and the dependent properties of the lattice constant, the energy bandgap and the Gibbs energy were presented for the (Ga,In)(As,Sb) quaternary compound semiconductor. On the bas...he expressions of the composition space and the dependent properties of the lattice constant, the energy bandgap and the Gibbs energy were presented for the (Ga,In)(As,Sb) quaternary compound semiconductor. On the basis of these expressions, a computer aided analysis system is set up for the design of ⅢⅤ compound semiconductor materials and growth processes. By using this system, a comprehensively optimized diagram is constructed through the projection of the optoelectronic properties (energy bandgap or wave length), in which the composition relations are matched to substrates and the miscibility gaps.展开更多
Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in ...Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in complex compositional spaces is challenging due to the lack of high-dimensional phase diagrams and the inefficiency of traditional trial-and-error methods.In this study,we developed a physics-informed machine learning(ML)framework that integrates a conditional variational autoencoder(CVAE)with an artificial neural network(ANN).This approach effectively addresses the challenges of data limitation and imbalance,enabling the high-throughput generation of B2 MPEIs.Using this framework,we successfully identified a wide range of B2 complex alloys,spanning quaternary to senary systems,with superior mechanical performance.This work not only demonstrates a significant advancement in the discovery of B2 MPEIs but also provides an accelerated pathway for their design and development.展开更多
The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a singl...The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.展开更多
基金Supported by the NNSF of China(10471039)the Natural Science Foundation of Zhejiang Province(M103 104)the Natural Science Foundation of Huzhou City(2005YZ02).
文摘This paper characterizes the boundedness and compactness of weighted composition operators between Bers-type space （or little Bers-type space） and Bergman space. Some estimates for the norm of weighted composition operators between those spaces are obtained.
文摘In this paper, using incremental equilibrium equation, the authors have studiedthe effeet of ultimate bearing capacity of every component on structuralstability, and discussed the stability analysis method for space compositestructures. With the help of the test results for the concrete filled ateel tubeskeleton of the long-spen RC arch bridse, it is proved that the proposed methodis accurate and reliable.
基金supported by grant DE-SC0023432 funded by the U.S. Department of Energy, Office of ScienceThis research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, using NERSC awards BES-ERCAP0033206, BES-ERCAP0025205, BES-ERCAP0025168, and BES-ERCAP0028072+1 种基金JM acknowledges support from the U.S. Department of Energy under contracts DE-SC0018410 (FES) and DE-SC0020314 (BES)ASB and JM acknowledge funding through a UCLA SoHub seed grant. SP acknowledges the Doctoral Finishing Fellowship awarded by the Graduate School at MTU. The authors would like to thank UCLA's Institute for Digital Research and Education (IDRE), the Superior HPC facility at MTU, the MRI GPU cluster at MTU for making available some of the computing resources used in this work. The authors acknowledge the use of the GPT-4o (OpenAI) model to polish the language and edit grammatical errors in some sections of this manuscript. The authors subsequently inspected, validated and edited the text generated by the AI model, before incorporation.
文摘Wepropose machine learning(ML)models to predict the electron density—the fundamental unknown of a material’s ground state—across the composition space of concentrated alloys.From this,other physical properties can be inferred,enabling accelerated exploration.A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species.To address this,we employ Bayesian Active Learning(AL),which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks.Compared to the strategic tessellation of the composition space,Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary(SiGeSn)and 1.7 for quaternary(CrFeCoNi)systems.We also introduce easy-to-optimize,body-attached-frame descriptors,which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases.Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.
文摘The study examined joint discontinuity spacing effect on drilling condition and performance in selected rocks in llorin and Ibese areas, Nigeria. Five samples for each rock type （gneiss and limestone） were tested in the laboratory for chemical, physical and mechanical properties. Dip direction and joint spacing were measured using compass clinometers. The chemical composition was determined using X-ray Fluores- cence （XRF） spectrometer. The results show that gneiss has SiO2 of 61.88g and limestone has CaO content of 52.3g. The average dry density of gneiss and limestone are 2.6 and 2.39 g/cm^3, respectively. The uniaxial compressive strength of gneiss and limestone are 195 and 93.83 MPa, respectively. These rocks are classified as strong and moderately strong rock. Gneiss and limestone have mean joint discontinuity spacing of 0.79 and 0.25 m, which classified them as moderate and wide joint spacing respectively. Joint spacing was correlated with specific energy, bit wear and uniaxial compressive using Statistical Package for Social Science （SPSS）. The regression model has multiple coefficient of correlation of R^2 = 0.791 and R^2 =0.995 for gneiss and limestone, respectively. The variation in joint spacing could be attributed to spe- cific energy, bit wear and uniaxial compressive strength which affect drilling condition and performance. Ultimately, as joint spacing gets closer, the drilling velocity increases, drill string will be stable.
文摘he expressions of the composition space and the dependent properties of the lattice constant, the energy bandgap and the Gibbs energy were presented for the (Ga,In)(As,Sb) quaternary compound semiconductor. On the basis of these expressions, a computer aided analysis system is set up for the design of ⅢⅤ compound semiconductor materials and growth processes. By using this system, a comprehensively optimized diagram is constructed through the projection of the optoelectronic properties (energy bandgap or wave length), in which the composition relations are matched to substrates and the miscibility gaps.
基金supported by university grants council(RGC),the Hong Kong government,through the general research fund(GRF)with the grant numbers of CityU 11201721 and CityU 11202924supported by the National Natural Science Foundation of China(Grant No.U20A20236).
文摘Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in complex compositional spaces is challenging due to the lack of high-dimensional phase diagrams and the inefficiency of traditional trial-and-error methods.In this study,we developed a physics-informed machine learning(ML)framework that integrates a conditional variational autoencoder(CVAE)with an artificial neural network(ANN).This approach effectively addresses the challenges of data limitation and imbalance,enabling the high-throughput generation of B2 MPEIs.Using this framework,we successfully identified a wide range of B2 complex alloys,spanning quaternary to senary systems,with superior mechanical performance.This work not only demonstrates a significant advancement in the discovery of B2 MPEIs but also provides an accelerated pathway for their design and development.
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500403)the Youth Fund of the National Natural Science Foundation of China(Grant no.52305443).We gratefully acknowledge HZWTECH for providing computational facilities.
文摘The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.
