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
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
This review presents a comprehensive overview of recent advances in supercapacitor electrode materials,with a particular emphasis on the synergistic interactions between electrode materials and electrolytes.Beyond the...This review presents a comprehensive overview of recent advances in supercapacitor electrode materials,with a particular emphasis on the synergistic interactions between electrode materials and electrolytes.Beyond the conventional categorization of materials such as carbon-based materials,conducting polymers,and metal oxides,we focus on emerging nanostructured systems including MXenes,transition metal dichalcogenides(TMDs),black phosphorus,and quantum dots.We highlight how engineering the electrode–electrolyte interface—through the use of ionic liquids,gelbased,and solid-state electrolytes—can enhance device performance by expanding voltage windows,improving cycling stability,and suppressing selfdischarge.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
Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen f...Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).展开更多
Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for ...Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.展开更多
In this article,the authors design a speaking unit based on needs analysis following Hutchinson and Waters'(1987) model.First,the rationale in designing this unit is introduced,which involves the teaching approach...In this article,the authors design a speaking unit based on needs analysis following Hutchinson and Waters'(1987) model.First,the rationale in designing this unit is introduced,which involves the teaching approach adopted and relevant theories in organizing the materials.Then,the teaching plan of this speaking unit is provided and some activities are designed to create an authentic and optimal situation for students to practice their speaking skill.展开更多
Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will pro...Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.展开更多
Based on the analyses of the severity of cutting process as well as the failure mechanisms of ceramic tools, a model for designing functionally gradient ceramic tool materials with symmetrical distribution is presente...Based on the analyses of the severity of cutting process as well as the failure mechanisms of ceramic tools, a model for designing functionally gradient ceramic tool materials with symmetrical distribution is presented, by which a Al 2O 3/(W,Ti)C ceramic tool material FG 2 was developed. Multi objective optimization method was employed in designing the compositional distribution of this ceramic tool material. The results of both continuous and intermittent cutting tests are indicative of the much better cutting behavior of the functionally gradient ceramic tool FG 2 than that of the common ceramic tool SG 4.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
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
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
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
The NiSiAlY material is a promising candidate to replace NiCrAlY,which can withstand the harsh saltspray conditions in marine environment.To efficiently design novel NiSiAlY alloys,this work establishes a thermodynami...The NiSiAlY material is a promising candidate to replace NiCrAlY,which can withstand the harsh saltspray conditions in marine environment.To efficiently design novel NiSiAlY alloys,this work establishes a thermodynamic dataset of the Al-Ni-Si-Y quaternary system using the CALPHAD(CALculation of PHAse Diagrams)approach.We employ this database to calculate and predict the phase constitutions and solidification behaviors of different NiSiAlY alloys concerning the content variations of Al and Si.We have further proposed the selection of the NiSiAlY alloys for serving in marine salt-spray environment with three constraints:(i)outstanding mechanical property;(ii)good high-temperature anti-oxidation;(iii)excellent corrosion resistance.This results in a compositional range of the NiSiAlY alloys with 1 wt%<w(Si)<5 wt%,11 wt%<w(Al)<20 wt%and w(Y)=1 wt%,which corresponds the L1_(2)+bcc B2+Ni_(5)Y ternary phase region at temperatures ranging from~500 to~1000℃.Our predictions are validated by key experiments,suggesting that the model-based description of the Al-Ni-Si-Y system can serve as a guidance to design the novel NiSiAlY alloys in resisting harsh salt-spray environment.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
Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth...Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.展开更多
Building processing,structure,and property(PSP)relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science.Recent techn...Building processing,structure,and property(PSP)relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science.Recent technological advancements in data acquisition and storage,microstructure characterization and reconstruction(MCR),machine learning(ML),materials modeling and simulation,data processing,manufacturing,and experimentation have significantly advanced researchers’abilities in building PSP relations and inverse material design.In this article,we examine these advancements from the perspective of design research.In particular,we introduce a data-centric approach whose fundamental aspects fall into three categories:design representation,design evaluation,and design synthesis.Developments in each of these aspects are guided by and benefit from domain knowledge.Hence,for each aspect,we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.展开更多
With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of i...With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.展开更多
As a new attempt to recycle minute metal scraps, the possibility of manufacturing design materials by semisolid extrusion processing was shown.A design material with an intended shape, such as a character or petal sha...As a new attempt to recycle minute metal scraps, the possibility of manufacturing design materials by semisolid extrusion processing was shown.A design material with an intended shape, such as a character or petal shape, was manufactured using minute metal scraps.Similarly, a design material with an intended color pattern for each metal, such as red copper in a white aluminum matrix, resembling grainlike wood, was manufactured by mixing two or more types of minute metal scrap.In addition, secondary design materials, which have engraved patterns on the surface of the target metal made by an electric discharge machine using the above primary design material as an electrode, were manufactured.展开更多
Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials a...Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.展开更多
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.
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supported by the Technology Innovation Program of the Ministry of Trade,Industry and Energy(MOTIE)(project No.RS-2023-00266568)。
文摘This review presents a comprehensive overview of recent advances in supercapacitor electrode materials,with a particular emphasis on the synergistic interactions between electrode materials and electrolytes.Beyond the conventional categorization of materials such as carbon-based materials,conducting polymers,and metal oxides,we focus on emerging nanostructured systems including MXenes,transition metal dichalcogenides(TMDs),black phosphorus,and quantum dots.We highlight how engineering the electrode–electrolyte interface—through the use of ionic liquids,gelbased,and solid-state electrolytes—can enhance device performance by expanding voltage windows,improving cycling stability,and suppressing selfdischarge.
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financially supported by the National Natural Science Foundation of China(Nos.52371007 and 52301042)the National Key R&D Program of China(No.2020YFB0704503)+2 种基金Shenzhen Science and Technology Program(No.SGDX20210823104002016)Guangdong Basic and Applied Basic Research Foundation(No.2021B1515120071)Shenzhen Basic Research Project(No.JCYJ20241202123504007)
文摘Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).
基金supported by the National Natural Science Foundation of China(52305388,BE0200030)Shanghai Pujiang Program(22PJ1407600)+1 种基金SJTU Explore X programShanghai Jiao Tong University Initiative Scientific Research Program(WH220402021)。
文摘Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.
文摘In this article,the authors design a speaking unit based on needs analysis following Hutchinson and Waters'(1987) model.First,the rationale in designing this unit is introduced,which involves the teaching approach adopted and relevant theories in organizing the materials.Then,the teaching plan of this speaking unit is provided and some activities are designed to create an authentic and optimal situation for students to practice their speaking skill.
文摘Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.
文摘Based on the analyses of the severity of cutting process as well as the failure mechanisms of ceramic tools, a model for designing functionally gradient ceramic tool materials with symmetrical distribution is presented, by which a Al 2O 3/(W,Ti)C ceramic tool material FG 2 was developed. Multi objective optimization method was employed in designing the compositional distribution of this ceramic tool material. The results of both continuous and intermittent cutting tests are indicative of the much better cutting behavior of the functionally gradient ceramic tool FG 2 than that of the common ceramic tool SG 4.
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.
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The financial support from Natural Science Foundation of Zhejiang Province(LQ20E010004)National Natural Science Foundation of China(51971235)+2 种基金National Science and Technology Major Project(2017-VII-0012-0107)Ningbo 3315 Innovation Team(No.2019A-18-C)CAS Pioneer Hundred Talents Program。
文摘The NiSiAlY material is a promising candidate to replace NiCrAlY,which can withstand the harsh saltspray conditions in marine environment.To efficiently design novel NiSiAlY alloys,this work establishes a thermodynamic dataset of the Al-Ni-Si-Y quaternary system using the CALPHAD(CALculation of PHAse Diagrams)approach.We employ this database to calculate and predict the phase constitutions and solidification behaviors of different NiSiAlY alloys concerning the content variations of Al and Si.We have further proposed the selection of the NiSiAlY alloys for serving in marine salt-spray environment with three constraints:(i)outstanding mechanical property;(ii)good high-temperature anti-oxidation;(iii)excellent corrosion resistance.This results in a compositional range of the NiSiAlY alloys with 1 wt%<w(Si)<5 wt%,11 wt%<w(Al)<20 wt%and w(Y)=1 wt%,which corresponds the L1_(2)+bcc B2+Ni_(5)Y ternary phase region at temperatures ranging from~500 to~1000℃.Our predictions are validated by key experiments,suggesting that the model-based description of the Al-Ni-Si-Y system can serve as a guidance to design the novel NiSiAlY alloys in resisting harsh salt-spray environment.
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Project supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.
基金support from the National Science Foundation(NSF)Cyberinfrastructure for Sustained Scientific Innovation program(OAC-1835782)the NSF Designing Materials to Revolutionize and Engineer Our Future program(CMMI-1729743)+1 种基金Center for Hierarchical Materials Design(NIST 70NANB19H005)at Northwestern Universitythe Advanced Research Projects Agency-Energy(APAR-E,DE-AR0001209)。
文摘Building processing,structure,and property(PSP)relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science.Recent technological advancements in data acquisition and storage,microstructure characterization and reconstruction(MCR),machine learning(ML),materials modeling and simulation,data processing,manufacturing,and experimentation have significantly advanced researchers’abilities in building PSP relations and inverse material design.In this article,we examine these advancements from the perspective of design research.In particular,we introduce a data-centric approach whose fundamental aspects fall into three categories:design representation,design evaluation,and design synthesis.Developments in each of these aspects are guided by and benefit from domain knowledge.Hence,for each aspect,we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.
基金financial supports from the National Key Research and Development Program of China(2018YFA0209600)the Natural Science Foundation of China(22022813,21878268,52075481)。
文摘With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.
文摘As a new attempt to recycle minute metal scraps, the possibility of manufacturing design materials by semisolid extrusion processing was shown.A design material with an intended shape, such as a character or petal shape, was manufactured using minute metal scraps.Similarly, a design material with an intended color pattern for each metal, such as red copper in a white aluminum matrix, resembling grainlike wood, was manufactured by mixing two or more types of minute metal scrap.In addition, secondary design materials, which have engraved patterns on the surface of the target metal made by an electric discharge machine using the above primary design material as an electrode, were manufactured.
基金Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)the National Key Research and Development Program of China(Grant No.2016YFB0700700)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),ChinaChina Post-doctoral Foundation(Grant No.7131705619).
文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
