CONSPECTUS:The data-driven paradigm,represented by the famous machine learning paradigm,is revolutionizing the way materials are discovered.The inductive nature of the data-driven approach gives it great speed of pred...CONSPECTUS:The data-driven paradigm,represented by the famous machine learning paradigm,is revolutionizing the way materials are discovered.The inductive nature of the data-driven approach gives it great speed of prediction but also brings with it a heavy reliance on material data.However,unlike its success with text and images,which are supported by big data,materials data tend to be small data.Building a large database of materials is a good solution but not a permanent one.The cost of materials data is much higher than that of text or images,and the size of the materials database at this stage is far from sufficient.We will continue to face a shortage of materials data for a long time to come,making small data approaches necessary for machine learning based materials discovery.展开更多
Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in ...Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.展开更多
The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progre...The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental work.Fortunately,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine learning.Together these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries.In this report,recent advances in material discovery methods are reviewed for energy devices.Three paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed critically.Key methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material design.Their applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.展开更多
The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which...The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning(ML)methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.展开更多
Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high co...Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.展开更多
The concept of the combinatorial discovery and optimization of new materials, and its background,importance, and application, as well as its current status in the world, are briefly reviewed in this paper.
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
Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,reveali...Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,revealing the properties,and guiding the experimental synthesis of materials.Rapid progress has been made in the integration of increasing database and improved computing power.Though some reviews present the development from their unique aspects,reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few.Here,the newest development is systematically reviewed in the field of AI empowered materials,reflecting advanced design of the intelligent systems for discovery,synthesis,prediction and validation of materials.First,background and mechanisms are briefed,after which the design for the AI systems with data,machine learning and automated laboratory included is illustrated.Next,strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials,and then,the design thought for future AI systems in material science is pointed out.Finally,some perspectives are put forward.展开更多
Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by vario...Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.展开更多
Ternary MAX phases,characterized by the chemical formula M₂AX,represent a group of layered materials with hexagonal lattices.These MAX phases have been the subject of extensive experimental and theoretical studies.For...Ternary MAX phases,characterized by the chemical formula M₂AX,represent a group of layered materials with hexagonal lattices.These MAX phases have been the subject of extensive experimental and theoretical studies.Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals,such as Rh,Ru,Pt,Pd,Co,and Ni,are unlikely to form.Here,we introduce an alternative family of orthorhombic and monoclinic materials,the LAX phases,which exhibit similarities to MAX phases in terms of their layered structure and A and X elements.However,LAX materials incorporate late transition metals in place of the early transition metals.Advanced techniques for predicting the crystal structure of materials,coupled with data-driven materials research and machine learning algorithms,were employed to investigate the stable structures containing transition metals from the last groups of the d-block elements.The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition,with 100 of these systems showing dynamic stability.An in-depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties,outperforming MAX phase counterparts in Young's modulus,stiffness,and hardness.These findings indicate that many LAX phase structures are viable candidates for future synthesis,highlighting the potential of heuristic-based structure searches in material discovery.展开更多
As the basis of modern industry, the roles materials play are becoming increasingly vital in this day and age. With many superior physical properties over conventional fluids, the low melting point liquid metal materi...As the basis of modern industry, the roles materials play are becoming increasingly vital in this day and age. With many superior physical properties over conventional fluids, the low melting point liquid metal material, especially room-temperature liquid metal, is recently found to be uniquely useful in a wide variety of emerging areas from energy, electronics to medical sciences. However, with the coming enormous utilization of such materials, serious issues also arise which urgently need to be addressed. A biggest concern to impede the large scale application of room-temperature liquid metal technologies is that there is currently a strong shortage of the materials and species available to meet the tough requirements such as cost, melting point, electrical and thermal conductivity, etc. Inspired by the Material Genome Initiative as issued in 2011 by the United States of America, a more specific and focused project initiative was proposed in this paper--the liquid metal material genome aimed to discover advanced new functional alloys with low melting point so as to fulfill various increasing needs. The basic schemes and road map for this new research program, which is expected to have a worldwide significance, were outlined. The theoretical strategies and experimental methods in the research and development of liquid metal material genome were introduced. Particularly, the calculation of phase diagram （CALPHAD） approach as a highly effective way for material design was discussed. Further, the first-principles （FP） calculation was suggested to combine with the statistical thermo- dynamics to calculate the thermodynamic functions so as to enrich the CALPHAD database of liquid metals. When the experimental data are too scarce to perform a regular treatment, the combination of FP calculation, cluster variation method （CVM） or molecular dynamics （MD）, and CALPHAD, referred to as the mixed FP-CVM- CALPHAD method can be a promising way to solve the problem. Except for the theoretical strategies, several parallel processing experimental methods were also analyzed, which can help improve the efficiency of finding new liquid metal materials and reducing the cost. The liquid metal material genome proposal as initiated in this paper will accelerate the process of finding and utilization of new functional materials.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experimen...The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations.Thus,it is imperative to develop a new method of accelerating the discovery and design process for novel materials.Recently,materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.In this review,we first outline the typical mode of and basic procedures for applying machine learning in materials science,and we classify and compare the main algorithms.Then,the current research status is reviewed with regard to applications of machine learning in material property prediction,in new materials discovery and for other purposes.Finally,we discuss problems related to machine learning in materials science,propose possible solutions,and forecast potential directions of future research.By directly combining computational studies with experiments,we hope to provide insight into the parameters that affect the properties of materials,thereby enabling more efficient and target-oriented research on materials discovery and design.展开更多
Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcem...Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.展开更多
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we ...Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.展开更多
The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse chal...The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.展开更多
We review our efforts to develop and implement robust computational approaches for exploring phase stability to facilitate the prediction-to-synthesis process of novel functional oxides.These efforts focus on a synerg...We review our efforts to develop and implement robust computational approaches for exploring phase stability to facilitate the prediction-to-synthesis process of novel functional oxides.These efforts focus on a synergy between(i)electronic structure calculations for properties predictions,(ii)phenomenological/empirical methods for examining phase stability as related to both phase segregation and temperature-dependent transitions and(iii)experimental validation through synthesis and characterization.We illustrate this philosophy by examining an inaugural study that seeks to discover novel functional oxides with high piezoelectric responses.Our results show progress towards developing a framework through which solid solutions can be studied to predict materials with enhanced properties that can be synthesized and remain active under device relevant conditions.展开更多
Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via hi...Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.展开更多
Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data ...Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data from both experiments and computations[2],process optimization for materials syntheses,management and monitoring of energy storage devices such as lithium batteries,and algorithm-optimized grid load forecasting.Looking back at recent pioneering works of AI-driven energy chemistry research,constructing a dataset with both large quantity and high quality is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.展开更多
基金supported by the National Key Research and Development Program of China(2021YFA1500703,2022YFA1503103,2022YFB3807200)Natural Science Foundation of China(22033002,T2321002,22373013)+2 种基金Natural Science Foundation of Jiangsu Province,Major Project(BK20232012,BK20222007)Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)the Fundamental Research Funds for the Central Universities.
文摘CONSPECTUS:The data-driven paradigm,represented by the famous machine learning paradigm,is revolutionizing the way materials are discovered.The inductive nature of the data-driven approach gives it great speed of prediction but also brings with it a heavy reliance on material data.However,unlike its success with text and images,which are supported by big data,materials data tend to be small data.Building a large database of materials is a good solution but not a permanent one.The cost of materials data is much higher than that of text or images,and the size of the materials database at this stage is far from sufficient.We will continue to face a shortage of materials data for a long time to come,making small data approaches necessary for machine learning based materials discovery.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFA1402304)the National Natural Science Foundation of China(Grant Nos.12034009,12374005,52288102,52090024,and T2225013)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for JLU Science and Technology Innovative Research Team.
文摘Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.
文摘The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental work.Fortunately,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine learning.Together these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries.In this report,recent advances in material discovery methods are reviewed for energy devices.Three paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed critically.Key methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material design.Their applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.
基金This work was financially supported by the National Natural Science Foundation of China(No.51627802)。
文摘The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning(ML)methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.
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The concept of the combinatorial discovery and optimization of new materials, and its background,importance, and application, as well as its current status in the world, are briefly reviewed in this paper.
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supported by the Hong Kong Polytechnic University(Project No.4-ZZW1,4-YWER,97D9,4-W443)。
文摘Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,revealing the properties,and guiding the experimental synthesis of materials.Rapid progress has been made in the integration of increasing database and improved computing power.Though some reviews present the development from their unique aspects,reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few.Here,the newest development is systematically reviewed in the field of AI empowered materials,reflecting advanced design of the intelligent systems for discovery,synthesis,prediction and validation of materials.First,background and mechanisms are briefed,after which the design for the AI systems with data,machine learning and automated laboratory included is illustrated.Next,strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials,and then,the design thought for future AI systems in material science is pointed out.Finally,some perspectives are put forward.
基金financially supported by the National Natural Science Foundation of China (Nos. 61971208, 61671225 and 51864027)the Yunnan Applied Basic Research Projects (No. 2018FA034)+2 种基金the Yunnan Reserve Talents of Young and Middleaged Academic and Technical Leaders (Shen Tao, 2018)the Yunnan Young Top Talents of Ten Thousands Plan (Shen Tao, Zhu Yan, Yunren Social Development No. 2018 73)the Scientific Research Foundation of Kunming University of Science and Technology (No. KKSY201703016)。
文摘Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.
基金Iran National Science Foundation,Grant/Award Number:4025794Japan Society for the Promotion of Science,Grant/Award Number:24K08211。
文摘Ternary MAX phases,characterized by the chemical formula M₂AX,represent a group of layered materials with hexagonal lattices.These MAX phases have been the subject of extensive experimental and theoretical studies.Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals,such as Rh,Ru,Pt,Pd,Co,and Ni,are unlikely to form.Here,we introduce an alternative family of orthorhombic and monoclinic materials,the LAX phases,which exhibit similarities to MAX phases in terms of their layered structure and A and X elements.However,LAX materials incorporate late transition metals in place of the early transition metals.Advanced techniques for predicting the crystal structure of materials,coupled with data-driven materials research and machine learning algorithms,were employed to investigate the stable structures containing transition metals from the last groups of the d-block elements.The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition,with 100 of these systems showing dynamic stability.An in-depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties,outperforming MAX phase counterparts in Young's modulus,stiffness,and hardness.These findings indicate that many LAX phase structures are viable candidates for future synthesis,highlighting the potential of heuristic-based structure searches in material discovery.
文摘As the basis of modern industry, the roles materials play are becoming increasingly vital in this day and age. With many superior physical properties over conventional fluids, the low melting point liquid metal material, especially room-temperature liquid metal, is recently found to be uniquely useful in a wide variety of emerging areas from energy, electronics to medical sciences. However, with the coming enormous utilization of such materials, serious issues also arise which urgently need to be addressed. A biggest concern to impede the large scale application of room-temperature liquid metal technologies is that there is currently a strong shortage of the materials and species available to meet the tough requirements such as cost, melting point, electrical and thermal conductivity, etc. Inspired by the Material Genome Initiative as issued in 2011 by the United States of America, a more specific and focused project initiative was proposed in this paper--the liquid metal material genome aimed to discover advanced new functional alloys with low melting point so as to fulfill various increasing needs. The basic schemes and road map for this new research program, which is expected to have a worldwide significance, were outlined. The theoretical strategies and experimental methods in the research and development of liquid metal material genome were introduced. Particularly, the calculation of phase diagram （CALPHAD） approach as a highly effective way for material design was discussed. Further, the first-principles （FP） calculation was suggested to combine with the statistical thermo- dynamics to calculate the thermodynamic functions so as to enrich the CALPHAD database of liquid metals. When the experimental data are too scarce to perform a regular treatment, the combination of FP calculation, cluster variation method （CVM） or molecular dynamics （MD）, and CALPHAD, referred to as the mixed FP-CVM- CALPHAD method can be a promising way to solve the problem. Except for the theoretical strategies, several parallel processing experimental methods were also analyzed, which can help improve the efficiency of finding new liquid metal materials and reducing the cost. The liquid metal material genome proposal as initiated in this paper will accelerate the process of finding and utilization of new functional materials.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.U1630134,51622207 and 51372228)the National Key Research and Development Program of China(Grant Nos.2017YFB0701600 and 2017YFB0701500)+2 种基金the Shanghai Institute of Materials Genome from the Shanghai Municipal Science and Technology Commission(Grant No.14DZ2261200)the Shanghai Municipal Education Commission(Grant No.14ZZ099)the Natural Science Foundation of Shanghai(Grant No.16ZR1411200).
文摘The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations.Thus,it is imperative to develop a new method of accelerating the discovery and design process for novel materials.Recently,materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.In this review,we first outline the typical mode of and basic procedures for applying machine learning in materials science,and we classify and compare the main algorithms.Then,the current research status is reviewed with regard to applications of machine learning in material property prediction,in new materials discovery and for other purposes.Finally,we discuss problems related to machine learning in materials science,propose possible solutions,and forecast potential directions of future research.By directly combining computational studies with experiments,we hope to provide insight into the parameters that affect the properties of materials,thereby enabling more efficient and target-oriented research on materials discovery and design.
基金National Natural Science Foundation of China[grant number 92270124,52073169]National Key Research and Development Program of China[grant number 2021YFB3802101]the Key Research Project of Zhejiang Laboratory[grant number 2021PE0AC02].
文摘Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.
基金supported by the Basic Science Center Project of National Natural Science Foundation of China(52388201)the National Natural Science Foundation of China(12334003)+4 种基金the National Science Fund for Distinguished Young Scholars(12025405)the National Key Basic Research and Development Program of China(2023YFA1406400)the Beijing Advanced Innovation Center for Future Chip(ICFC)the Beijing Advanced Innovation Center for Materials Genome Engineeringfunded by the Shuimu Tsinghua Scholar program。
文摘Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.
基金This work was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project no.15222018).
文摘The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.
基金the US Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(VRC,JRM),and the Office of Science Early Career Research Program(VRC).SPB acknowledges support from the US National Science Foundation under Grant No.DMR-1037898CAB acknowledges support from the Laboratory Directed Research and Development program of Oak Ridge National Laboratory,managed by UT-Battelle,LLC,for the U.S.Department of Energy.This research used resources of the National Energy Research Scientific Computing Center,which is supported by the Office of Science of the US Department of Energy under Contract No.DE-AC02-05CH11231.
文摘We review our efforts to develop and implement robust computational approaches for exploring phase stability to facilitate the prediction-to-synthesis process of novel functional oxides.These efforts focus on a synergy between(i)electronic structure calculations for properties predictions,(ii)phenomenological/empirical methods for examining phase stability as related to both phase segregation and temperature-dependent transitions and(iii)experimental validation through synthesis and characterization.We illustrate this philosophy by examining an inaugural study that seeks to discover novel functional oxides with high piezoelectric responses.Our results show progress towards developing a framework through which solid solutions can be studied to predict materials with enhanced properties that can be synthesized and remain active under device relevant conditions.
基金This work was supported by the NSF(award number 2030128,2110033)NASA SC Space Grant Consortium REAP Program(Award No.:521383-RP-SC004)+1 种基金SC EPSCoR/IDeA Program under NSF OIA-1655740(23-GC01)ASPIRE grant from the Office of the Vice President for Research at the University of South Carolina(project 80005046).
文摘Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.
基金supported by the National Key Research and Development Program of China(2021YFB2500300)the National Natural Science Foundation of China(T2322015,92472101,22393903,22393900,52394170)the Beijing Municipal Natural Science Foundation(L247015,L233004)。
文摘Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data from both experiments and computations[2],process optimization for materials syntheses,management and monitoring of energy storage devices such as lithium batteries,and algorithm-optimized grid load forecasting.Looking back at recent pioneering works of AI-driven energy chemistry research,constructing a dataset with both large quantity and high quality is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.
