In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of ...We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.展开更多
Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN mo...Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.展开更多
Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coord...Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.展开更多
Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective ...Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.展开更多
In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reve...In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reverse space-time nonlocal Mel'nikov equation and the nonlocal twodimensional nonlinear Schr?dinger(NLS)equation.By the PINN method,we successfully derive a data-driven two soliton solution,lump solution and rogue wave solution.Numerical simulation results indicate that the error range between the data-driven solution and the exact solution is relatively small,which verifies the effectiveness of the PINN deep learning method for solving high dimensional nonlocal equations.Moreover,the parameter discovery of the partial reverse space-time nonlocal Mel'nikov equation is analysed in terms of its soliton solution for the first time.展开更多
In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector mo...In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector module was presented.A high-precision forward prediction model based on deep neural networks was developed to implement these two parts.Of utmost importance,domain knowledge-guided inverse design networks(DKIDNs)and regular inverse design networks(RIDNs)were also developed.The forward prediction model possesses a coefficient of determination(R^(2))of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set.Furthermore,the DKIDNs model exhibits superior performance compared to the RIDNs model.It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.展开更多
Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application p...Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.展开更多
The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primaril...The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases.However,the known materials only scratch the surface of the extensive array of possibilities within the realm of materials.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high co...Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.展开更多
Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-...Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.展开更多
To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generali...To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generalized nonlinear Schrödinger equation and the rate equations.However,this approach is burdened by substantial computational demands,resulting in significant time expenditures.In the context of intelligent laser optimization and inverse design,the necessity for numerous simulations further exacerbates this issue,highlighting the need for fast and accurate simulation methodologies.Here,we introduce an end-to-end model augmented with active learning(E2E-AL)with decent generalization through different dedicated embedding methods over various parameters.On an identical computational platform,the artificial intelligence–driven model is 2000 times faster than the conventional simulation method.Benefiting from the active learning strategy,the E2E-AL model achieves decent precision with only two-thirds of the training samples compared with the case without such a strategy.Furthermore,we demonstrate a multi-objective inverse design of the CPA systems enabled by the E2E-AL model.The E2E-AL framework manifests the potential of becoming a standard approach for the rapid and accurate modeling of ultrafast lasers and is readily extended to simulate other complex systems.展开更多
Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the t...Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.展开更多
Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an...Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.展开更多
This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the chara...This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the characteristic function and resolvent of this third-order differential operator.Secondly,by using the expression for the resolvent of the operator,we prove that the spectrum for this operator consists of simple eigenvalues and a finite number of eigenvalues with multiplicity 2.Finally,we solve the inverse problem for this operator,which states that the non-local potential function can be reconstructed from four spectra.Specially,we prove the Ambarzumyan theorem and indicate that odd or even potential functions can be reconstructed by three spectra.展开更多
Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under h...Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.展开更多
Endocrine disruptors such as bisphenol A(BPA)adversely affect the environment and human health.Laccases are used for the efficient biodegradation of various persistent organic pollutants in an environmentally safe man...Endocrine disruptors such as bisphenol A(BPA)adversely affect the environment and human health.Laccases are used for the efficient biodegradation of various persistent organic pollutants in an environmentally safe manner.However,the direct application of free laccases is generally hindered by short enzyme lifetimes,non-reusability,and the high cost of a single use.In this study,laccases were immobilized on a novel magnetic threedimensional poly(ethylene glycol)diacrylate(PEGDA)-chitosan(CS)inverse opal hydrogel(LAC@MPEGDA@CS@IOH).The immobilized laccase showed significant improvement in the BPA degradation performance and superior storage stability compared with the free laccase.91.1%of 100 mg/L BPA was removed by the LAC@MPEGDA@CS@IOH in 3 hr,whereas only 50.6%of BPA was removed by the same amount of the free laccase.Compared with the laccase,the outstanding BPA degradation efficiency of the LAC@MPEGDA@CS@IOH was maintained over a wider range of pH values and temperatures.Moreover,its relative activity of was maintained at 70.4%after 10 cycles,and the system performed well in actual water matrices.This efficientmethod for preparing immobilized laccases is simple and green,and it can be used to further develop ecofriendly biocatalysts to remove organic pollutants from wastewater.展开更多
The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of ...The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.展开更多
This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distr...This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distribution function that increases with an increase in energy at zero electron energy.The inverse EEDF plays a central role in the problem of negative conductivity.Based on the previously obtained criterion for the formation of an inverse EEDF in a spatially inhomogeneous plasma,a heuristic method is proposed that allows one to avoid resource-intensive calculations for spatially two-dimensional(2D)kinetic modeling on a large array of different glow discharges.It is shown that the conditions for EEDF inversion can be realized in two-chamber discharge structures due to violating the known Boltzmann distribution for electron density.The theoretical conclusions are validated by numerical modeling of lowpressure two-chamber inductively-coupled plasma(ICP)discharges in the COMSOL Multiphysics environment.As a result,areas of conditions with inverse EEDF were found for subsequent detailed kinetic analysis and experimental studies.展开更多
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
文摘We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.
基金supported by the Fundamental Research Funds for the Central Universities (No.2024JBZY021)the National Natural Science Foundation of China (No.61575018)。
文摘Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.
基金supported by the National Key Research and Development Program of China(No.2019YFA0405300)the National Natural Science Foundation of China(No.12272405).
文摘Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.
基金supported by the National Natural Science Foundation of China(Grant Nos.62375137 and 62175114).
文摘Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.
文摘In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reverse space-time nonlocal Mel'nikov equation and the nonlocal twodimensional nonlinear Schr?dinger(NLS)equation.By the PINN method,we successfully derive a data-driven two soliton solution,lump solution and rogue wave solution.Numerical simulation results indicate that the error range between the data-driven solution and the exact solution is relatively small,which verifies the effectiveness of the PINN deep learning method for solving high dimensional nonlocal equations.Moreover,the parameter discovery of the partial reverse space-time nonlocal Mel'nikov equation is analysed in terms of its soliton solution for the first time.
基金supported by the National Natural Science Foundation of China(No.52471184)the Science and Technology Major Project of Hunan Province,China(No.2019GK1012)+1 种基金the Postgraduate Scientific Research Innovation Project of Xiangtan University,China(No.XDCX2023Y174)the Postgraduate Scientific Research Innovation Project of Xiangtan University,China(No.XDCX2023Y173).
文摘In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector module was presented.A high-precision forward prediction model based on deep neural networks was developed to implement these two parts.Of utmost importance,domain knowledge-guided inverse design networks(DKIDNs)and regular inverse design networks(RIDNs)were also developed.The forward prediction model possesses a coefficient of determination(R^(2))of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set.Furthermore,the DKIDNs model exhibits superior performance compared to the RIDNs model.It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.
基金supported by the National Key R and D Program of China(No.2022YFF0604801)the National Natural Science Foundation of China(Nos.62271056,62171186,62201037)+3 种基金the Technology Innovation Center of Infrared Remote Sensing Metrology Technology of State Administration for Market Regulation(No.AKYKF2423)the Beijing Natural Science Foundation of China-Haidian Original Innovation Joint Fund(No.L222042)the Open Research Fund of State Key Laboratory of Millimeter Waves(No.K202326)the 111 Project of China(No.B14010).
文摘Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.62476278,12434009,and 12204533)the National Key R&D Program of China(Grant No.2024YFA1408601)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302402)。
文摘The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases.However,the known materials only scratch the surface of the extensive array of possibilities within the realm of materials.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金funded by theNationalNatural Science Foundation of China(52061020)Major Science and Technology Projects in Yunnan Province(202302AG050009)Yunnan Fundamental Research Projects(202301AV070003).
文摘Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.
基金supported by the National Natural Science Foundation of China(grant numbers 11972287 and 12072266)the State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ23106)+1 种基金the National Key Laboratory of Aircraft Configuration Design(No.2023-JCJQ-LB-070)the Fundamental Research Funds for the Central Universities.
文摘Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.
基金supported by the National Natural Science Foundation of China(Grant Nos.62227821,62025503,and 62205199).
文摘To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generalized nonlinear Schrödinger equation and the rate equations.However,this approach is burdened by substantial computational demands,resulting in significant time expenditures.In the context of intelligent laser optimization and inverse design,the necessity for numerous simulations further exacerbates this issue,highlighting the need for fast and accurate simulation methodologies.Here,we introduce an end-to-end model augmented with active learning(E2E-AL)with decent generalization through different dedicated embedding methods over various parameters.On an identical computational platform,the artificial intelligence–driven model is 2000 times faster than the conventional simulation method.Benefiting from the active learning strategy,the E2E-AL model achieves decent precision with only two-thirds of the training samples compared with the case without such a strategy.Furthermore,we demonstrate a multi-objective inverse design of the CPA systems enabled by the E2E-AL model.The E2E-AL framework manifests the potential of becoming a standard approach for the rapid and accurate modeling of ultrafast lasers and is readily extended to simulate other complex systems.
基金supported by the National Key Research and Development Project(Grant No.2023YFB4705300)the National Natural Science Foundation of China(NSFC)(Grant Nos.62205200 and 62375168)the Natural Science Foundation of Shanghai(Grant No.22ZR1431600)。
文摘Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.
基金Supported by the National Natural Science Foundation of China (Grant Nos.52088102 and 51879287)National Key Research and Development Program of China (Grant No.2022YFB2602301)。
文摘Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.
基金supported by the Tianjin Municipal Science and Technology Program of China(No.23JCZDJC00070)。
文摘This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the characteristic function and resolvent of this third-order differential operator.Secondly,by using the expression for the resolvent of the operator,we prove that the spectrum for this operator consists of simple eigenvalues and a finite number of eigenvalues with multiplicity 2.Finally,we solve the inverse problem for this operator,which states that the non-local potential function can be reconstructed from four spectra.Specially,we prove the Ambarzumyan theorem and indicate that odd or even potential functions can be reconstructed by three spectra.
基金supported by the Key Technologies R&D Program of China National Offshore Oil Corporation(No.KJGG2021-0504).
文摘Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.
基金supported by the National Key Research and Development Program of China(Nos.2022YFC3703700 and 2021YFA0910300)the National Natural Science Foundation of China(No.22125606)the Special Project of Ecological Environmental Technology for Carbon Dioxide Emissions Peak and Carbon Neutrality(No.RCEES-TDZ-2021-21).
文摘Endocrine disruptors such as bisphenol A(BPA)adversely affect the environment and human health.Laccases are used for the efficient biodegradation of various persistent organic pollutants in an environmentally safe manner.However,the direct application of free laccases is generally hindered by short enzyme lifetimes,non-reusability,and the high cost of a single use.In this study,laccases were immobilized on a novel magnetic threedimensional poly(ethylene glycol)diacrylate(PEGDA)-chitosan(CS)inverse opal hydrogel(LAC@MPEGDA@CS@IOH).The immobilized laccase showed significant improvement in the BPA degradation performance and superior storage stability compared with the free laccase.91.1%of 100 mg/L BPA was removed by the LAC@MPEGDA@CS@IOH in 3 hr,whereas only 50.6%of BPA was removed by the same amount of the free laccase.Compared with the laccase,the outstanding BPA degradation efficiency of the LAC@MPEGDA@CS@IOH was maintained over a wider range of pH values and temperatures.Moreover,its relative activity of was maintained at 70.4%after 10 cycles,and the system performed well in actual water matrices.This efficientmethod for preparing immobilized laccases is simple and green,and it can be used to further develop ecofriendly biocatalysts to remove organic pollutants from wastewater.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.62476278,12434009,and 12204533)supported by the National Key R&D Program of China(Grant No.2024YFA1408601)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302402)。
文摘The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.
基金supported by the National Key R&D Program of China(No.2022YFE0204100)National Natural Science Foundation of China(Nos.12205067 and 12375199)the Fundamental Research Funds for the Central University(No.HIT.D?J.2023178)。
文摘This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distribution function that increases with an increase in energy at zero electron energy.The inverse EEDF plays a central role in the problem of negative conductivity.Based on the previously obtained criterion for the formation of an inverse EEDF in a spatially inhomogeneous plasma,a heuristic method is proposed that allows one to avoid resource-intensive calculations for spatially two-dimensional(2D)kinetic modeling on a large array of different glow discharges.It is shown that the conditions for EEDF inversion can be realized in two-chamber discharge structures due to violating the known Boltzmann distribution for electron density.The theoretical conclusions are validated by numerical modeling of lowpressure two-chamber inductively-coupled plasma(ICP)discharges in the COMSOL Multiphysics environment.As a result,areas of conditions with inverse EEDF were found for subsequent detailed kinetic analysis and experimental studies.
