Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co...Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.展开更多
An expert system prototype for fibre-reinforced plastic matrix (FRP) composite material design, ESFRP, has been developed. The system consists of seven main functional parts: a general inference engine, a set of knowl...An expert system prototype for fibre-reinforced plastic matrix (FRP) composite material design, ESFRP, has been developed. The system consists of seven main functional parts: a general inference engine, a set of knowledge bases, a material properties algorithm base, an explanation engine, various data bases, several function models and the user interface. The ESFRP can simulate human experts to make design scheme for fibre-reinforced plastics design, FRP layered plates design and FRP typical engineering components design. It can also predict the material properties and make strength analysis according to the micro and macro mechanics of composite materials. A satisfied result can be gained through the reiterative design.展开更多
The purpose of this paper is to investigate the application of topology description function （TDF） in material design. Using TDF to describe the topology of the microstructure, the formulation and the solving techni...The purpose of this paper is to investigate the application of topology description function （TDF） in material design. Using TDF to describe the topology of the microstructure, the formulation and the solving technique of the design problem of materials with prescribed mechanical properties are presented. By presenting the TDF as the sum of a series of basis functions determined by parameters, the topology optimization of material microstructure is formulated as a size optimization problem whose design variables are parameters of TDF basis functions and independent of the mesh of the design domain. By this method, high quality topologies for describing the distribution of constituent material in design domain can be obtained and checkerboard problem often met in the variable density method is avoided. Compared with the conventional level set method, the optimization problem can be solved simply by existing optimization techniques without the process to solve the ＇Hamilton-Jacobi-type＇ equation by the difference method. The method proposed is illustrated with two 2D examples. One gives the unit cell with positive Poisson＇s ratio, the other with negative Poisson＇s ratio. The examples show the method based on TDF is effective for material design.展开更多
To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project,a new paving layer material,Guss-mastic asphalt(GMA),was proposed in this paper by combining the advantages of two types of cast asphalt mix...To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project,a new paving layer material,Guss-mastic asphalt(GMA),was proposed in this paper by combining the advantages of two types of cast asphalt mixtures:mastic asphalt(MA)and Guss asphalt(GA).Based on the characteristics of GMA,to simulate its actual production process,this study developed a small-simulated cooker mixing equipment.Moreover,the flow degree,60C dynamic stability,and impact toughness were proposed to be used to evaluate the construction and ease,high temperature stability,and fatigue resistance of GMA cast asphalt mixtures,respectively.Moreover,the quality control standards for GMA paving materials by indoor tests,field trial mix GMA material performance tests,and accelerated loading tests were finalized.The study showed that the developed simulated cooker yielded consistent mixing results in the same working environment as the engineering cooker device.Increasing the coarse aggregate incorporation rate,coarsening the mastic epure(ME)gradation composition,and using a smaller oil to stone ratio can reduce the flowability of the GMA materials to varying degrees.The four-point bending fatigue life and impact toughness of the different GMA materials are correlated well.A mobility of<20 s,60C dynamic stability of 400–800 times/mm,15C impact toughness of400 N⋅mm,and cooker car mixing temperature control standard of 210C–230C form an appropriate control index system for the design and production of GMA cast asphalt mixtures.Simultaneously,accelerated loading tests verified the accuracy and reliability of the quality control index system that has been used in the GMA paving project of the Hong Kong–Zhuhai–Macao Bridge deck and has achieved good application results.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-...To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.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
To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In thi...To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.展开更多
Segmented thermoelectric generators(STEGs)can exhibit present superior performance than those of the conventional thermoelectric generators.Thermal and electrical contact resistances exist between the thermoelectric m...Segmented thermoelectric generators(STEGs)can exhibit present superior performance than those of the conventional thermoelectric generators.Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg.This may significantly hinder performance improvement.In this study,a five-layer STEG with three pairs of thermoelectric(TE)materials was investigated considering the thermal and electrical contact resistances on the material contact surface.The STEG performance under different contact resistances with various combinations of TE materials were analyzed.The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning.Based on the genetic algorithm,for each contact resistance combination,the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency.To reveal the underlying mechanism that determines the heat-to-electrical performance,the total electrical resistance,output voltage,ZT value,and temperature distribution under each optimized scenario were analyzed.The STEG can augment the heat-to-electricity performance only at small contact resistances.A large contact resistance significantly reduces the performance.At an electrical contact resistance of RE=10^(-3) K⋅m^(2)⋅W^(-1) and thermal contact resistance of RT=10-8Ω⋅m^(2),the maximum electric power was reduced to 5.71 mW(90.86 mW without considering the contact resistance).And the maximum energy conversion efficiency is lowered to 2.54%(12.59%without considering the contact resistance).展开更多
With the deep integration of electrochemical research with energy,environment,catalysis,and other fields,more and more new electrochemical catalytic reactions have entered our research field.Alloy catalysts have recen...With the deep integration of electrochemical research with energy,environment,catalysis,and other fields,more and more new electrochemical catalytic reactions have entered our research field.Alloy catalysts have recently emerged as a new type of nanomaterial due to the rapid development of kinetic controlled synthesis technology.These materials offer several advantages over monometallic catalysts,including larger element combinations,complex geometries,bifunctional sites,and reduced use of precious metals.This paper provides a review of alloy electrocatalysts that are designed and prepared specifically for electrocatalytic applications.The use of alloy materials in electrocatalyst design is also discussed,highlighting their widespread application in this field.First,various synthesis methods and synthesis mechanisms are systematically summarized.Following that,by correlating the properties of materials with the structure,relevant strategies toward advanced alloy electrocatalysts including composition regulation,size,morphology,surface engineering,defect engineering,interface engineering and strain engineering are classified.In addition,the important electrocatalytic applications and mechanisms of alloy electrocatalysts are described and summarized.Finally,the current challenges and prospects regarding the development of alloy nanomaterials are proposed.This review serves as a springboard from a fundamental understanding of alloy structural dynamics to design and various applications of electrocatalysts,particularly in energy and environmental sustainability.展开更多
The optimized design of simple cross-truss and column lattice structures was carried out by the SolidWorks simulation module.The effective density of the structure was calculated according to the weight reduction requ...The optimized design of simple cross-truss and column lattice structures was carried out by the SolidWorks simulation module.The effective density of the structure was calculated according to the weight reduction requirements proposed by the project.Then,the vari-ation curve between the maximum bearing stress of the unit structure and the structural variables was obtained by simulation.Meanwhile,the mathematical equation between the maximum bearing stress and the structural variables could be obtained through MATLAB fitting.The results indicated that with the decrease in the number of cells,the compressive strength of the prepared column lattice increased(400 to 4 cells,compressive strength 29 MPa to 160 MPa).However,the yield strength increased with the number of cells.The compression strength of the simple cross-truss lattice samples indicated an increase trend with the decrease of the pillar size(an increase of the number of units),reaching 91 MPa(pillar diameter 0.52 mm,number of units 25).While the yield strength increased with the increasing of the number of units.In addition,the additive manufacturing processes of simple cubic lattice and simple cross-pillar lattice were investigated using selective laser melting.The compression performance obtained from the experiment is compared with the simulation results,which are in good agreement.The results of this paper can provide an important reference for optimizing design of lattice materials.展开更多
AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database...AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database of Solid-State Electrolyte(DDSE)demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development.These databases facilitate data standardization,high-throughput screening,and cross-disciplinary collaboration,addressing key challenges in materials informatics.As AI techniques advance,materials databases are expected to play an increasingly vital role in accelerating research and innovation.展开更多
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
Light-to-thermal conversion materials(LTCMs)have been of great interest to researchers due to their impressive energy conversion capacity and wide range of applications in biomedical,desalination,and synergistic catal...Light-to-thermal conversion materials(LTCMs)have been of great interest to researchers due to their impressive energy conversion capacity and wide range of applications in biomedical,desalination,and synergistic catalysis.Given the limited advances in existing materials(metals,semiconductors,π-conjugates),researchers generally adopt the method of constructing complex systems and hybrid structures to optimize performance and achieve multifunctional integration.However,the development of LTCMs is still in its infancy as the physical mechanism of light-to-thermal conversion is unclear.In this review,we proposed design strategies for efficient LTCMs by analyzing the physical process of light-tothermal conversion.First,we analyze the nature of light absorption and heat generation to reveal the physical processes of light-to-thermal conversion.Then,we explain the light-to-thermal conversion mechanisms of metallic,semiconducting andπ-conjugated LCTMs,and propose new material design strategies and performance improvement methods.Finally,we summarize the challenges and prospects of LTCMs in emerging applications such as solar water evaporation and photothermal catalysis.展开更多
AlSi10Mg fabricated by selective laser melting(SLM)had a unique network-like silicon-rich structure,and the mechanism for its formation was explained by molecular dynamics(MD)simulations.The effects of the silicon-ric...AlSi10Mg fabricated by selective laser melting(SLM)had a unique network-like silicon-rich structure,and the mechanism for its formation was explained by molecular dynamics(MD)simulations.The effects of the silicon-rich phase and Mg-containing structure on corrosion were studied by first-principles methods.According to the simulations,corrosion resistant materials were designed,samples with laser powers of 150 W,200 W and 250 W were fabricated.The results indicated that a local thermal gradient during laser printing caused Si segregation,and the rapid cooling rate lead to a large number of subgrains,which assisted precipitation.The difference in potential caused galvanic corrosion,and a structure with low work function in the molten pool caused pitting.The corrosion resistance of materials processed with a high laser power increased.展开更多
An effective method to design structural Left-handed material（LHM） was proposed. A commercial finite element software HFSS and S-parameter retrieval method were used to determine the effective constitutive parameter...An effective method to design structural Left-handed material（LHM） was proposed. A commercial finite element software HFSS and S-parameter retrieval method were used to determine the effective constitutive parameters of the metamaterials, and topology optimization technique was introduced to design the microstructure configurations of the materials with desired electromagnetic characteristics. The material considered was a periodic array of dielectric substrates attached with metal film pieces. By controlling the arrangements of the metal film pieces in the design domain, the potential microstructure with desired electromagnetic characteristics can be obtained finally. Two different LHMs were obtained with maximum bandwidth of negative refraction, and the experimental results show that negative refractive indices appear while the metamaterials have simultaneously negative permittivity and negative permeability. Topology optimization technique is found to be an effective tool for configuration design of LHMs.展开更多
This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples...This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.展开更多
Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narr...Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.展开更多
Frequent insulin injections remain the primary method for controlling the blood glucose level of individuals with diabetes mellitus but are associated with low compliance.Accordingly,oral administration has been ident...Frequent insulin injections remain the primary method for controlling the blood glucose level of individuals with diabetes mellitus but are associated with low compliance.Accordingly,oral administration has been identified as a highly desirable alternative due to its non-invasive nature.However,the harsh gastrointestinal environment and physical intestinal barriers pose significant challenges to achieving optimal pharmacological bioavailability of insulin.As a result,researchers have developed a range of materials to improve the efficiency of oral insulin delivery over the past few decades.In this review,we summarize the latest advances in material design that aim to enhance insulin protection,permeability,and glucoseresponsive release.We also explore the opportunities and challenges of using these materials for oral insulin delivery.展开更多
基金financially supported by the Technology Development Fund of China Academy of Machinery Science and Technology(No.170221ZY01)。
文摘Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.
基金The work is funded by Heilongjiang Natural Science Foundation of China(No.E9803).
文摘An expert system prototype for fibre-reinforced plastic matrix (FRP) composite material design, ESFRP, has been developed. The system consists of seven main functional parts: a general inference engine, a set of knowledge bases, a material properties algorithm base, an explanation engine, various data bases, several function models and the user interface. The ESFRP can simulate human experts to make design scheme for fibre-reinforced plastics design, FRP layered plates design and FRP typical engineering components design. It can also predict the material properties and make strength analysis according to the micro and macro mechanics of composite materials. A satisfied result can be gained through the reiterative design.
基金Project supported by the National Natural Science Foundation of China (No.10332010) the Innovative Research Team Program (No. 10421202) the National Basic Research Program of China (No. 2006CB601205) and the Program for New Century Excellent Talents in Universities of China (2004).
文摘The purpose of this paper is to investigate the application of topology description function （TDF） in material design. Using TDF to describe the topology of the microstructure, the formulation and the solving technique of the design problem of materials with prescribed mechanical properties are presented. By presenting the TDF as the sum of a series of basis functions determined by parameters, the topology optimization of material microstructure is formulated as a size optimization problem whose design variables are parameters of TDF basis functions and independent of the mesh of the design domain. By this method, high quality topologies for describing the distribution of constituent material in design domain can be obtained and checkerboard problem often met in the variable density method is avoided. Compared with the conventional level set method, the optimization problem can be solved simply by existing optimization techniques without the process to solve the ＇Hamilton-Jacobi-type＇ equation by the difference method. The method proposed is illustrated with two 2D examples. One gives the unit cell with positive Poisson＇s ratio, the other with negative Poisson＇s ratio. The examples show the method based on TDF is effective for material design.
文摘To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project,a new paving layer material,Guss-mastic asphalt(GMA),was proposed in this paper by combining the advantages of two types of cast asphalt mixtures:mastic asphalt(MA)and Guss asphalt(GA).Based on the characteristics of GMA,to simulate its actual production process,this study developed a small-simulated cooker mixing equipment.Moreover,the flow degree,60C dynamic stability,and impact toughness were proposed to be used to evaluate the construction and ease,high temperature stability,and fatigue resistance of GMA cast asphalt mixtures,respectively.Moreover,the quality control standards for GMA paving materials by indoor tests,field trial mix GMA material performance tests,and accelerated loading tests were finalized.The study showed that the developed simulated cooker yielded consistent mixing results in the same working environment as the engineering cooker device.Increasing the coarse aggregate incorporation rate,coarsening the mastic epure(ME)gradation composition,and using a smaller oil to stone ratio can reduce the flowability of the GMA materials to varying degrees.The four-point bending fatigue life and impact toughness of the different GMA materials are correlated well.A mobility of<20 s,60C dynamic stability of 400–800 times/mm,15C impact toughness of400 N⋅mm,and cooker car mixing temperature control standard of 210C–230C form an appropriate control index system for the design and production of GMA cast asphalt mixtures.Simultaneously,accelerated loading tests verified the accuracy and reliability of the quality control index system that has been used in the GMA paving project of the Hong Kong–Zhuhai–Macao Bridge deck and has achieved good application results.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
基金the National Natural Science Foundation of China(Nos.52370083 and 52170088)Sichuan Science and Technology Program(No.2024NSFTD0014)Key R&D Program of Heilongjiang Province(No.2023ZX02C01)for financial support。
文摘To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.
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(No.52102470).
文摘To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.
基金supported by the National Natural Science Foundation of China(Grant No.:52176070).
文摘Segmented thermoelectric generators(STEGs)can exhibit present superior performance than those of the conventional thermoelectric generators.Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg.This may significantly hinder performance improvement.In this study,a five-layer STEG with three pairs of thermoelectric(TE)materials was investigated considering the thermal and electrical contact resistances on the material contact surface.The STEG performance under different contact resistances with various combinations of TE materials were analyzed.The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning.Based on the genetic algorithm,for each contact resistance combination,the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency.To reveal the underlying mechanism that determines the heat-to-electrical performance,the total electrical resistance,output voltage,ZT value,and temperature distribution under each optimized scenario were analyzed.The STEG can augment the heat-to-electricity performance only at small contact resistances.A large contact resistance significantly reduces the performance.At an electrical contact resistance of RE=10^(-3) K⋅m^(2)⋅W^(-1) and thermal contact resistance of RT=10-8Ω⋅m^(2),the maximum electric power was reduced to 5.71 mW(90.86 mW without considering the contact resistance).And the maximum energy conversion efficiency is lowered to 2.54%(12.59%without considering the contact resistance).
基金supported by the National Natural Science Foundation of China(No.52072153)the Postdoctoral Science Foundation of China(No.2021M690023)+2 种基金the Postdoctoral Science Foundation of Jiangsu Province(No.2021K176B)the Graduate Research Innovation Program of Jiangsu Provincial(Nos.KYCX22_3694 and KYCX23_3649)the Zhenjiang Key R&D Programmes(No.SH2021021)。
文摘With the deep integration of electrochemical research with energy,environment,catalysis,and other fields,more and more new electrochemical catalytic reactions have entered our research field.Alloy catalysts have recently emerged as a new type of nanomaterial due to the rapid development of kinetic controlled synthesis technology.These materials offer several advantages over monometallic catalysts,including larger element combinations,complex geometries,bifunctional sites,and reduced use of precious metals.This paper provides a review of alloy electrocatalysts that are designed and prepared specifically for electrocatalytic applications.The use of alloy materials in electrocatalyst design is also discussed,highlighting their widespread application in this field.First,various synthesis methods and synthesis mechanisms are systematically summarized.Following that,by correlating the properties of materials with the structure,relevant strategies toward advanced alloy electrocatalysts including composition regulation,size,morphology,surface engineering,defect engineering,interface engineering and strain engineering are classified.In addition,the important electrocatalytic applications and mechanisms of alloy electrocatalysts are described and summarized.Finally,the current challenges and prospects regarding the development of alloy nanomaterials are proposed.This review serves as a springboard from a fundamental understanding of alloy structural dynamics to design and various applications of electrocatalysts,particularly in energy and environmental sustainability.
基金supported by the National Natural Science Foundation of China(Grant No.52101058,51875541).
文摘The optimized design of simple cross-truss and column lattice structures was carried out by the SolidWorks simulation module.The effective density of the structure was calculated according to the weight reduction requirements proposed by the project.Then,the vari-ation curve between the maximum bearing stress of the unit structure and the structural variables was obtained by simulation.Meanwhile,the mathematical equation between the maximum bearing stress and the structural variables could be obtained through MATLAB fitting.The results indicated that with the decrease in the number of cells,the compressive strength of the prepared column lattice increased(400 to 4 cells,compressive strength 29 MPa to 160 MPa).However,the yield strength increased with the number of cells.The compression strength of the simple cross-truss lattice samples indicated an increase trend with the decrease of the pillar size(an increase of the number of units),reaching 91 MPa(pillar diameter 0.52 mm,number of units 25).While the yield strength increased with the increasing of the number of units.In addition,the additive manufacturing processes of simple cubic lattice and simple cross-pillar lattice were investigated using selective laser melting.The compression performance obtained from the experiment is compared with the simulation results,which are in good agreement.The results of this paper can provide an important reference for optimizing design of lattice materials.
文摘AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database of Solid-State Electrolyte(DDSE)demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development.These databases facilitate data standardization,high-throughput screening,and cross-disciplinary collaboration,addressing key challenges in materials informatics.As AI techniques advance,materials databases are expected to play an increasingly vital role in accelerating research and innovation.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
基金the financial support from the National Natural Science Foundation of China(Grant Nos.52272153,52032004)the KLOMT Key Laboratory Open Project(2022KLOMT02-05)。
文摘Light-to-thermal conversion materials(LTCMs)have been of great interest to researchers due to their impressive energy conversion capacity and wide range of applications in biomedical,desalination,and synergistic catalysis.Given the limited advances in existing materials(metals,semiconductors,π-conjugates),researchers generally adopt the method of constructing complex systems and hybrid structures to optimize performance and achieve multifunctional integration.However,the development of LTCMs is still in its infancy as the physical mechanism of light-to-thermal conversion is unclear.In this review,we proposed design strategies for efficient LTCMs by analyzing the physical process of light-tothermal conversion.First,we analyze the nature of light absorption and heat generation to reveal the physical processes of light-to-thermal conversion.Then,we explain the light-to-thermal conversion mechanisms of metallic,semiconducting andπ-conjugated LCTMs,and propose new material design strategies and performance improvement methods.Finally,we summarize the challenges and prospects of LTCMs in emerging applications such as solar water evaporation and photothermal catalysis.
基金the National Key Research and Development program of China(No.2017YFB 0702300)Fundamental Research Funds for the Central Universities(No.FRF-TP-18-002B2)National Natural Science Foundation of China(No.51671029)。
文摘AlSi10Mg fabricated by selective laser melting(SLM)had a unique network-like silicon-rich structure,and the mechanism for its formation was explained by molecular dynamics(MD)simulations.The effects of the silicon-rich phase and Mg-containing structure on corrosion were studied by first-principles methods.According to the simulations,corrosion resistant materials were designed,samples with laser powers of 150 W,200 W and 250 W were fabricated.The results indicated that a local thermal gradient during laser printing caused Si segregation,and the rapid cooling rate lead to a large number of subgrains,which assisted precipitation.The difference in potential caused galvanic corrosion,and a structure with low work function in the molten pool caused pitting.The corrosion resistance of materials processed with a high laser power increased.
基金Funded by the National Natural Science Foundation of China (Nos.90605002, 90816025 and 10721062)the National Basic Research Programof China (No. 2006CB601205)
文摘An effective method to design structural Left-handed material（LHM） was proposed. A commercial finite element software HFSS and S-parameter retrieval method were used to determine the effective constitutive parameters of the metamaterials, and topology optimization technique was introduced to design the microstructure configurations of the materials with desired electromagnetic characteristics. The material considered was a periodic array of dielectric substrates attached with metal film pieces. By controlling the arrangements of the metal film pieces in the design domain, the potential microstructure with desired electromagnetic characteristics can be obtained finally. Two different LHMs were obtained with maximum bandwidth of negative refraction, and the experimental results show that negative refractive indices appear while the metamaterials have simultaneously negative permittivity and negative permeability. Topology optimization technique is found to be an effective tool for configuration design of LHMs.
基金Project supported by the National Natural Science Foundation of China(Grant No.51772321)the Beijing Science and Technology Project(Grant No.D171100005517001)+1 种基金the National Key Research and Development Plan(Grant No.2017YFB0701602)the Youth Innovation Promotion Association(Grant No.2016005)
文摘This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.
基金financial support from the Natural Science Foundation of China(No.21973064 to DX and No.22173064 to MY).
文摘Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.
基金supported by the grants from National Key R&D Program of China(2022YFE0202200)Zhejiang University’s start-up packages,Kunpeng program from Zhejiang Province,Fundamental Research Funds for the Central Universities(2021FZZX001-46)+1 种基金the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(SN-ZJU-SIAS-009)JDRF(grant no.2-SRA-2021-1064-M-B).
文摘Frequent insulin injections remain the primary method for controlling the blood glucose level of individuals with diabetes mellitus but are associated with low compliance.Accordingly,oral administration has been identified as a highly desirable alternative due to its non-invasive nature.However,the harsh gastrointestinal environment and physical intestinal barriers pose significant challenges to achieving optimal pharmacological bioavailability of insulin.As a result,researchers have developed a range of materials to improve the efficiency of oral insulin delivery over the past few decades.In this review,we summarize the latest advances in material design that aim to enhance insulin protection,permeability,and glucoseresponsive release.We also explore the opportunities and challenges of using these materials for oral insulin delivery.
