Specimens of materials for prospective use in chambers of nuclear fusion reactors with inertial plasma confinement,namely,W,ODS steels,Eurofer 97 steel,a number of ceramics,etc.,have been irradiated by dense plasma fo...Specimens of materials for prospective use in chambers of nuclear fusion reactors with inertial plasma confinement,namely,W,ODS steels,Eurofer 97 steel,a number of ceramics,etc.,have been irradiated by dense plasma focus devices and a laser in the Q-switchedmode of operation with a wide range of parameters,including some that noticeably exceeded those expected in reactors.By means of 1-ns laser interferometry and neutron measurements,the characteristics of plasma streams and fast ion beams,as well as the dynamics of their interaction with solid-state targets,have been investigated.3D profilometry,optical and scanning electron microscopy,atomic emission spectroscopy,X-ray elemental and structural analyses,and precise weighing of specimens before and after irradiation have provided data on the roughening threshold and the susceptibility to damage of the materials under investigation.Analysis of the results,together with numerical modeling,has revealed the important role of shock waves in the damage processes.It has been shown that a so-called integral damage factor may be used only within restricted ranges of the irradiation parameters.It has also been found that in the irradiation regime with well-developed gasdynamic motion of secondary plasma,the overall amount of radiation energy is spent preferentially either on removing large masses of cool matter from the material surface or on heating a small amount of plasma to high temperature(and,consequently,imparting to it a high velocity),depending on the power flux density and characteristics of the pulsed irradiation.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires gr...Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires great human creativity in order to realize the desired properties and thereby the required functionalities through design.Such properties and functionalities are not necessarily available in nature,and their design can break through the existing materials ideology.This paper reviews progress in metamaterials research over the past 20 years in terms of the materials innovations that have achieved the designation of “meta.” In particular,we discuss future trends in metamaterials in the fields of both fundamental science and engineering.展开更多
Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the emai...Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the email address from xingcai@stanford.edu to drtea1@wteao.com.Also,the corresponding author’s affiliation can be expanded.展开更多
The authors are very sorry for their carelessness that a wrong Fig.9 was uploaded,and a corrected one has been shown below:This corrigendum does not affect the overall structure and analysis process of the study.The a...The authors are very sorry for their carelessness that a wrong Fig.9 was uploaded,and a corrected one has been shown below:This corrigendum does not affect the overall structure and analysis process of the study.The authors would like to apologize for any inconvenience caused.展开更多
The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in ...The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in the field of life sciences[1−7].Among them,supramolecular materials have garnered increasing attention in life sciences owing to their distinctive self-assembly capabilities and intelligent responsiveness[8−12].展开更多
NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
The advancement of materials has played a pivotal role in the advancement of human civilization,and the emergence of artificial intelligence(AI)-empowered materials science heralds a new era with substantial potential...The advancement of materials has played a pivotal role in the advancement of human civilization,and the emergence of artificial intelligence(AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy,environment,and biomedical concerns in a sustainable manner.The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly,energy-efficient,and conducive to human well-being.This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications.We anticipate that AI technology will be extensively utilized in material research and development,thereby expediting the growth and implementation of novel materials.AI will serve as a catalyst for materials innovation,and in turn,advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science.Through the synergistic collaboration between AI and materials science,we stand to realize a future propelled by advanced AI-powered materials.展开更多
The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the o...The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the originally published article,Fig.1(d)and Fig.7 were adapted from previously published figures in the cited literature.展开更多
Corresponding author’s name was incorrectly written as“Dadang Guo”instead of“Dagang Guo”.The correct author name should be“Dagang Guo”.The authors would like to apologise for any inconvenience caused.
Peri-implantitis is a bacterial infection that causes soft tissue inflammatory lesions and alveolar bone resorption,ultimately resulting in implant failure.Dental implants for clinical use barely have antibacterial pr...Peri-implantitis is a bacterial infection that causes soft tissue inflammatory lesions and alveolar bone resorption,ultimately resulting in implant failure.Dental implants for clinical use barely have antibacterial properties,and bacterial colonization and biofilm formation on the dental implants are major causes of peri-implantitis.Treatment strategies such as mechanical debridement and antibiotic therapy have been used to remove dental plaque.However,it is particularly important to prevent the occurrence of peri-implantitis rather than treatment.Therefore,the current research spot has focused on improving the antibacterial properties of dental implants,such as the construction of specific micro-nano surface texture,the introduction of diverse functional coatings,or the application of materials with intrinsic antibacterial properties.The aforementioned antibacterial surfaces can be incorporated with bioactive molecules,metallic nanoparticles,or other functional components to further enhance the osteogenic properties and accelerate the healing process.In this review,we summarize the recent developments in biomaterial science and the modification strategies applied to dental implants to inhibit biofilm formation and facilitate bone-implant integration.Furthermore,we summarized the obstacles existing in the process of laboratory research to reach the clinic products,and propose corresponding directions for future developments and research perspectives,so that to provide insights into the rational design and construction of dental implants with the aim to balance antibacterial efficacy,biological safety,and osteogenic property.展开更多
In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike t...In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.展开更多
Cement stands as a dominant contributor to global energy consumption and carbon emissions in the construction industry.With the upgrading of infrastructure and the improvement of building standards,traditional cement ...Cement stands as a dominant contributor to global energy consumption and carbon emissions in the construction industry.With the upgrading of infrastructure and the improvement of building standards,traditional cement fails to reconcile ecological responsibility with advanced functional performance.By incorporating tailored fillers into cement matrices,the resulting composites achieve enhanced thermoelectric(TE)conversion capabilities.These materials can harness solar radiation from building envelopes and recover waste heat from indoor thermal gradients,facilitating bidirectional energy conversion.This review offers a comprehensive and timely overview of cementbased thermoelectric materials(CTEMs),integrating material design,device fabrication,and diverse applications into a holistic perspective.It summarizes recent advancements in TE performance enhancement,encompassing fillers optimization and matrices innovation.Additionally,the review consolidates fabrication strategies and performance evaluations of cement-based thermoelectric devices(CTEDs),providing detailed discussions on their roles in monitoring and protection,energy harvesting,and smart building.We also address sustainability,durability,and lifecycle considerations of CTEMs,which are essential for real-world deployment.Finally,we outline future research directions in materials design,device engineering,and scalable manufacturing to foster the practical application of CTEMs in sustainable and intelligent infrastructure.展开更多
As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.Ho...As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.However,the true effectiveness of these advancements lies in the seamless integration of alternative semiconductors tailored for next-generation transistors.In this review,we highlight key advances that enhance both scalability and switching performance by leveraging emerging semiconductor materials.Among the most promising candidates are 2D van der Waals semiconductors,Mott insulators,and amorphous oxide semiconductors,which offer not only unique electrical properties but also low-power operation and high carrier mobility.Additionally,we explore the synergistic interactions between these novel semiconductors and advanced gate dielectrics,including high-K materials,ferroelectrics,and atomically thin hexagonal boron nitride layers.Beyond introducing these novel material configurations,we address critical challenges such as leakage current and long-term device reliability,which become increasingly crucial as transistors scale down to atomic dimensions.Through concrete examples showcasing the potential of these materials in transistors,we provide key insights into overcoming fundamental obstacles—such as device reliability,scaling down limitations,and extended applications in artificial intelligence—ultimately paving the way for the development of future transistor technologies.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.展开更多
Porous organic frameworks(POFs)have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials,both in their pristine state and when subjected to v...Porous organic frameworks(POFs)have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials,both in their pristine state and when subjected to various chemical and structural modifications.Metal–organic frameworks,covalent organic frameworks,and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties,such as high crystallinity,intrinsic porosity,unique structural regularity,diverse functionality,design flexibility,and outstanding stability.This review provides an overview of the state-of-the-art research on base-stable POFs,emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials.Thereafter,the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements.It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.展开更多
Perovskite solar cells represent a promising third-generation photovoltaic technology with low fabrication cost and high power conversion efficiency.In light of the rapid development of perovskite materials and device...Perovskite solar cells represent a promising third-generation photovoltaic technology with low fabrication cost and high power conversion efficiency.In light of the rapid development of perovskite materials and devices,a systematic survey on the latest advancements covering a broad range of related work is urgently needed.This review summarizes the recent major advances in the research of perovskite solar cells from a material science perspective.The discussed topics include the devices based on different type of perovskites(organic-inorganic hybrid,all-inorganic,and lead-free perovskite and perovskite quantum dots),the properties of perovskite defects,different type of charge transport materials(organic,polymeric,and inorganic hole transport materials and inorganic and organic electron transport materials),counter electrodes,and interfacial materials used to improve the efficiency and stability of devices.Most discussions focus on the key progresses reported within the recent five years.Meanwhile,the major issues limiting the production of perovskite solar cells and the prospects for the future development of related materials are discussed.展开更多
Phase-field method,as a powerful and popular approach to predict the mesoscale microstructure evolution in various materials science,provides a bridge from atomic-scale methods to the macroscale and has been widely us...Phase-field method,as a powerful and popular approach to predict the mesoscale microstructure evolution in various materials science,provides a bridge from atomic-scale methods to the macroscale and has been widely used at an ever-increasing rate.This paper aims to briefly review the origin,basic idea,and development of phase-field models in a historical manner.The focus is placed on the classical and state-of-the-art applications in China,including liquid–solid,solid–solid,gas–solid,ferroelectrics/ferromagnetics phase transformation,and crack propagation-fracture.After introducing the academic activities in the phase-field community in China,some suggestions for the future development directions of phase-field method are finally mentioned.展开更多
The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our a...The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.展开更多
Laser-heated diamond-anvil cell (LHDAC) is emerging as the most suitable, economical and versatile tool for the measurement of a large spectrum of physical properties of materials under extreme pressure and temperatur...Laser-heated diamond-anvil cell (LHDAC) is emerging as the most suitable, economical and versatile tool for the measurement of a large spectrum of physical properties of materials under extreme pressure and temperature conditions. In this review, the recent developments in the instrumentation, pressure and temperature measurement techniques, results of experimental investigations from the literature were discussed. Also, the future scope of the technique in various avenues of science was explored.展开更多
文摘Specimens of materials for prospective use in chambers of nuclear fusion reactors with inertial plasma confinement,namely,W,ODS steels,Eurofer 97 steel,a number of ceramics,etc.,have been irradiated by dense plasma focus devices and a laser in the Q-switchedmode of operation with a wide range of parameters,including some that noticeably exceeded those expected in reactors.By means of 1-ns laser interferometry and neutron measurements,the characteristics of plasma streams and fast ion beams,as well as the dynamics of their interaction with solid-state targets,have been investigated.3D profilometry,optical and scanning electron microscopy,atomic emission spectroscopy,X-ray elemental and structural analyses,and precise weighing of specimens before and after irradiation have provided data on the roughening threshold and the susceptibility to damage of the materials under investigation.Analysis of the results,together with numerical modeling,has revealed the important role of shock waves in the damage processes.It has been shown that a so-called integral damage factor may be used only within restricted ranges of the irradiation parameters.It has also been found that in the irradiation regime with well-developed gasdynamic motion of secondary plasma,the overall amount of radiation energy is spent preferentially either on removing large masses of cool matter from the material surface or on heating a small amount of plasma to high temperature(and,consequently,imparting to it a high velocity),depending on the power flux density and characteristics of the pulsed irradiation.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金supported by the National Key Research and Development Program of China (2022YFB3806000)the Key Program of National Natural Science Foundation of China (52332006)。
文摘Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires great human creativity in order to realize the desired properties and thereby the required functionalities through design.Such properties and functionalities are not necessarily available in nature,and their design can break through the existing materials ideology.This paper reviews progress in metamaterials research over the past 20 years in terms of the materials innovations that have achieved the designation of “meta.” In particular,we discuss future trends in metamaterials in the fields of both fundamental science and engineering.
文摘Correction to:Nano-Micro Letters(2025)17:135 https://doi.org/10.1007/s40820-024-01634-8 Following publication of the original article[1],the authors reported that the corresponding author would like to update the email address from xingcai@stanford.edu to drtea1@wteao.com.Also,the corresponding author’s affiliation can be expanded.
文摘The authors are very sorry for their carelessness that a wrong Fig.9 was uploaded,and a corrected one has been shown below:This corrigendum does not affect the overall structure and analysis process of the study.The authors would like to apologize for any inconvenience caused.
基金supported by the National Natural Science Foundation of China(22101043)the Fundamental Research Funds for the Central Universities(N2205013,N232410019,N2405013)+3 种基金Natural Science Foundation of Liaoning Province(2023-MSBA-068)the Opening Fund of State Key Laboratory of Heavy Oil Processing(SKLHOP202203006)the Key Laboratory of Functional Molecular Solids,Ministry of Education(FMS2023005)Northeastern University。
文摘The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in the field of life sciences[1−7].Among them,supramolecular materials have garnered increasing attention in life sciences owing to their distinctive self-assembly capabilities and intelligent responsiveness[8−12].
基金supported by the Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
基金the support from Stanfordthe support from CUHKHKU
文摘The advancement of materials has played a pivotal role in the advancement of human civilization,and the emergence of artificial intelligence(AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy,environment,and biomedical concerns in a sustainable manner.The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly,energy-efficient,and conducive to human well-being.This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications.We anticipate that AI technology will be extensively utilized in material research and development,thereby expediting the growth and implementation of novel materials.AI will serve as a catalyst for materials innovation,and in turn,advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science.Through the synergistic collaboration between AI and materials science,we stand to realize a future propelled by advanced AI-powered materials.
文摘The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the originally published article,Fig.1(d)and Fig.7 were adapted from previously published figures in the cited literature.
文摘Corresponding author’s name was incorrectly written as“Dadang Guo”instead of“Dagang Guo”.The correct author name should be“Dagang Guo”.The authors would like to apologise for any inconvenience caused.
基金supported by the National Key Research and Development Program of China(2023YFC2412600)the National Natural Science Foundation of China(52271243,52171233,82370924,82170929)+3 种基金the Beijing Natural Science Foundation(L212014)the Beijing Nova Program(20230484459)the National Clinical Key Discipline Construction Project(PKUSSNKP-T202103)the Research Foundation of Peking University School and Hospital of Stomatology(PKSS20230104).
文摘Peri-implantitis is a bacterial infection that causes soft tissue inflammatory lesions and alveolar bone resorption,ultimately resulting in implant failure.Dental implants for clinical use barely have antibacterial properties,and bacterial colonization and biofilm formation on the dental implants are major causes of peri-implantitis.Treatment strategies such as mechanical debridement and antibiotic therapy have been used to remove dental plaque.However,it is particularly important to prevent the occurrence of peri-implantitis rather than treatment.Therefore,the current research spot has focused on improving the antibacterial properties of dental implants,such as the construction of specific micro-nano surface texture,the introduction of diverse functional coatings,or the application of materials with intrinsic antibacterial properties.The aforementioned antibacterial surfaces can be incorporated with bioactive molecules,metallic nanoparticles,or other functional components to further enhance the osteogenic properties and accelerate the healing process.In this review,we summarize the recent developments in biomaterial science and the modification strategies applied to dental implants to inhibit biofilm formation and facilitate bone-implant integration.Furthermore,we summarized the obstacles existing in the process of laboratory research to reach the clinic products,and propose corresponding directions for future developments and research perspectives,so that to provide insights into the rational design and construction of dental implants with the aim to balance antibacterial efficacy,biological safety,and osteogenic property.
基金supported by the National Natural Science Foundation of China(Nos.52571028,52301029)the Fundamental Research Funds for the Central Universities(No.06500165)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515140006)the AVIC Heavy Machinery Innovation Fund(ZJQT-2025-06)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.
基金supported by the National Natural Science Foundation of China(No.52242305).
文摘Cement stands as a dominant contributor to global energy consumption and carbon emissions in the construction industry.With the upgrading of infrastructure and the improvement of building standards,traditional cement fails to reconcile ecological responsibility with advanced functional performance.By incorporating tailored fillers into cement matrices,the resulting composites achieve enhanced thermoelectric(TE)conversion capabilities.These materials can harness solar radiation from building envelopes and recover waste heat from indoor thermal gradients,facilitating bidirectional energy conversion.This review offers a comprehensive and timely overview of cementbased thermoelectric materials(CTEMs),integrating material design,device fabrication,and diverse applications into a holistic perspective.It summarizes recent advancements in TE performance enhancement,encompassing fillers optimization and matrices innovation.Additionally,the review consolidates fabrication strategies and performance evaluations of cement-based thermoelectric devices(CTEDs),providing detailed discussions on their roles in monitoring and protection,energy harvesting,and smart building.We also address sustainability,durability,and lifecycle considerations of CTEMs,which are essential for real-world deployment.Finally,we outline future research directions in materials design,device engineering,and scalable manufacturing to foster the practical application of CTEMs in sustainable and intelligent infrastructure.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT),South Korea(RS-2024-00421181)financially supported in part by National R&D Program(2021M3H4A3A02086430)through NRF(National Research Foundation of Korea)funded by Ministry of Science and ICT+2 种基金the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(No.GTL25021-210)The Inter-University Semiconductor Research Center,Institute of Engineering Research,and Soft Foundry Institute at Seoul National University provided research facilities for this workhe grant by the National Research Foundation of Korea(NSF)supported by the Korea government(MIST)(RS-2025-16903034)。
文摘As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.However,the true effectiveness of these advancements lies in the seamless integration of alternative semiconductors tailored for next-generation transistors.In this review,we highlight key advances that enhance both scalability and switching performance by leveraging emerging semiconductor materials.Among the most promising candidates are 2D van der Waals semiconductors,Mott insulators,and amorphous oxide semiconductors,which offer not only unique electrical properties but also low-power operation and high carrier mobility.Additionally,we explore the synergistic interactions between these novel semiconductors and advanced gate dielectrics,including high-K materials,ferroelectrics,and atomically thin hexagonal boron nitride layers.Beyond introducing these novel material configurations,we address critical challenges such as leakage current and long-term device reliability,which become increasingly crucial as transistors scale down to atomic dimensions.Through concrete examples showcasing the potential of these materials in transistors,we provide key insights into overcoming fundamental obstacles—such as device reliability,scaling down limitations,and extended applications in artificial intelligence—ultimately paving the way for the development of future transistor technologies.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
基金supported by the Fundamental-Core National Project of the National Research Foundation(NRF)funded by the Ministry of Science and ICT,Republic of Korea(2022R1F1A1072739).
文摘Porous organic frameworks(POFs)have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials,both in their pristine state and when subjected to various chemical and structural modifications.Metal–organic frameworks,covalent organic frameworks,and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties,such as high crystallinity,intrinsic porosity,unique structural regularity,diverse functionality,design flexibility,and outstanding stability.This review provides an overview of the state-of-the-art research on base-stable POFs,emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials.Thereafter,the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements.It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.
基金supported by the National Natural Science Foundation of China(21975264,21925112,21875122,61935016,92056119,61935016,21771008)Beijing Natural Science Foundation(2191003)+1 种基金the Youth Innovation Promotion Association Chinese Academy of Sciences,the National Key Research and Development Project funding from the Ministry of Science and Technology of China(2021YFB3800100,2021YFB3800101,2020YFB1506400)the Basic and Applied Basic Research Foundation of Guangdong Province(2019B1515120083)。
文摘Perovskite solar cells represent a promising third-generation photovoltaic technology with low fabrication cost and high power conversion efficiency.In light of the rapid development of perovskite materials and devices,a systematic survey on the latest advancements covering a broad range of related work is urgently needed.This review summarizes the recent major advances in the research of perovskite solar cells from a material science perspective.The discussed topics include the devices based on different type of perovskites(organic-inorganic hybrid,all-inorganic,and lead-free perovskite and perovskite quantum dots),the properties of perovskite defects,different type of charge transport materials(organic,polymeric,and inorganic hole transport materials and inorganic and organic electron transport materials),counter electrodes,and interfacial materials used to improve the efficiency and stability of devices.Most discussions focus on the key progresses reported within the recent five years.Meanwhile,the major issues limiting the production of perovskite solar cells and the prospects for the future development of related materials are discussed.
基金the National Natural Science Foundation of China(Nos.52074246,52201146,52205429,52275390,U1904214)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)+1 种基金the Key Research and Development Program of Shanxi Province(No.202102050201011)L.Z.acknowledges the Natural Science Foundation of Hunan Province for Distinguished Young Scholars(No.2021JJ10062).
文摘Phase-field method,as a powerful and popular approach to predict the mesoscale microstructure evolution in various materials science,provides a bridge from atomic-scale methods to the macroscale and has been widely used at an ever-increasing rate.This paper aims to briefly review the origin,basic idea,and development of phase-field models in a historical manner.The focus is placed on the classical and state-of-the-art applications in China,including liquid–solid,solid–solid,gas–solid,ferroelectrics/ferromagnetics phase transformation,and crack propagation-fracture.After introducing the academic activities in the phase-field community in China,some suggestions for the future development directions of phase-field method are finally mentioned.
基金supported by the Informatization Plan of the Chinese Academy of Sciences (Grant No. CASWX2023SF-0101)the Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-7025)+1 种基金the Youth Innovation Promotion Association CAS (Grant No. 2021167)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000)。
文摘The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.
文摘Laser-heated diamond-anvil cell (LHDAC) is emerging as the most suitable, economical and versatile tool for the measurement of a large spectrum of physical properties of materials under extreme pressure and temperature conditions. In this review, the recent developments in the instrumentation, pressure and temperature measurement techniques, results of experimental investigations from the literature were discussed. Also, the future scope of the technique in various avenues of science was explored.
