Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enh...Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enhancement via traditional trial-and-error methods.In this work,we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechani-cal properties.We first established machine learning models to predict Young's modulus,tensile strength,and elongation at break to explore the chemical space containing thousands of candidate structures.The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films.The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations,and the structural rationale was revealed by"gene"analysis and feature importance evaluation.This work provides a cost-effective strategy for designing polyimide films withenhancedmechanical properties.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
Metallic glasses(MGs)have attracted extensive attention in the past decades due to their unique chem-ical,physical and mechanical properties promising for a wide range of engineering applications.A thor-ough understan...Metallic glasses(MGs)have attracted extensive attention in the past decades due to their unique chem-ical,physical and mechanical properties promising for a wide range of engineering applications.A thor-ough understanding of their structure-property relationships is the key to the development of novel MGs with desirable performance.New strategies,as proposed by Materials Genome Initiative(MGI),construct a new paradigm for high-throughput materials discovery and design,and are being increas-ingly implemented in the search of new MGs.While a few reports have summarized the application of high-throughput and/or machine learning techniques,a comprehensive assessment of materials genome strategies for developing MGs is still missing.Herein,this paper aims to present a timely overview of key advances in this fascinating subject,as well as current challenges and future opportunities.A holistic approach is used to cover the related topics,including high-throughput preparation and characterization of MGs,and data-driven machine learning strategies for accelerating the development of novel MGs.Fi-nally,future research directions and perspectives for MGI-assisted design of MGs are also proposed and surmised.展开更多
Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data...Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.展开更多
This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples...This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.展开更多
High-throughput computational materials design provides one efficient solution to accelerate the discovery and development of functional materials. Its core concept is to build a large quantum materials repository and...High-throughput computational materials design provides one efficient solution to accelerate the discovery and development of functional materials. Its core concept is to build a large quantum materials repository and to search for target materials with desired properties via appropriate materials descriptors in a high-throughput fashion, which shares the same idea with the materials genome approach. This article reviews recent progress of discovering and developing new functional materials using high-throughput computational materials design approach. Emphasis is placed on the rational design of high-throughput screening procedure and the development of appropriate materials descriptors, concentrating on the electronic and magnetic properties of functional materials for various types of industrial applications in nanoelectronics.展开更多
The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composit...The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.展开更多
Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-sourc...Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-source graph deep learning library for materials science and chemistry.Built on top of the popular Deep Graph Library(DGL)and Python Materials Genomics(Pymatgen)packages,MatGL is designed to be an extensible“batteries-included”library for developing advanced model architectures for materials property predictions and interatomic potentials.At present,MatGL has efficient implementations for both invariant and equivariant graph deep learning models,including the Materials 3-body Graph Network(M3GNet),MatErials Graph Network(MEGNet),Crystal Hamiltonian Graph Network(CHGNet),TensorNet and SO3Net architectures.MatGL also provides several pretrained foundation potentials(FPs)with coverage of the entire periodic table,and property prediction models for out-of-box usage,benchmarking and fine-tuning.Finally,MatGL integrates with PyTorch Lightning to enable efficient model training.展开更多
This essay discusses some preliminary thoughts on the development of a rational and modular approach for molecular design in soft matter engineering and proposes ideas of structural and functional synthons for advance...This essay discusses some preliminary thoughts on the development of a rational and modular approach for molecular design in soft matter engineering and proposes ideas of structural and functional synthons for advanced functional materials. It echoes the Materials Genome Initiative by practicing a tentative retro-functional analysis （RFA） scheme. The importance of hierarchical structures in transferring and amplifying molecular functions into macroscopic properties is recognized and emphasized. According to the role of molecular segments in final materials, there are two types of building blocks： structural synthon and functional synthon. Guided by a specific structure for a desired function, these synthons can be modularly combined in various ways to construct molecular scaffolds. Detailed molecular structures are then deduced, designed and synthesized precisely and modularly. While the assembled structure and property may deviate from the original design, the study may allow further refinement of the molecular design toward the target function, The strategy has been used in the development of soft fullerene materials and other giant molecules. There are a few aspects that are not yet well addressed： （1） function and structure are not fully decoupled and （2） the assembled hierarchical structures are sensitive to secondary interactions and molecular geometries across different length scales. Nevertheless, the RFA approach provides a starting point and an alternative thinking pathway by provoking creativity with considerations from both chemistry and physics. This is particularly useful for engineering soft matters with supramolecular lattice formation, as in giant molecules, where the synthons are relatively independent of each other.展开更多
Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing.Polymeric material genome engineering(PMGE)is becoming a vital platform for the intelli...Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing.Polymeric material genome engineering(PMGE)is becoming a vital platform for the intelligent manufacturing of polymeric materials.However,the development of PMGE is still in its infancy,and many issues remain to be addressed.In this perspective,we elaborate on the PMGE concepts,summarize the state-of-the-art research and achievements,and highlight the challenges and prospects in this field.In particular,we focus on property estimation approaches,including property proxy prediction and machine learning prediction of polymer properties.The potential engineering applications of PMGE are discussed,including the fields of advanced composites,polymeric materials for communications,and integrated circuits.展开更多
Fast synthesis and screening of materials are vital to the advance of materials science and are an essential component of the Materials Genome Initiative. Here we use copper-oxide superconductors as an example to demo...Fast synthesis and screening of materials are vital to the advance of materials science and are an essential component of the Materials Genome Initiative. Here we use copper-oxide superconductors as an example to demonstrate the power of integrating combinatorial molecular beam epitaxy synthesis with high-throughput electric transport measurements. Leveraging this method, we have generated a phase diagram with more than 800 compositions in order to unravel the doping dependence of interface superconductivity. In another application of the same method, we have studied the superconductorto-insulator quantum phase transition with unprecedented accuracy in tuning the chemical doping level.展开更多
Due to ever-increasing concern about safety issues in using alkali metal ionic batteries, all solid-state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high-performance ASSBs lies in ...Due to ever-increasing concern about safety issues in using alkali metal ionic batteries, all solid-state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high-performance ASSBs lies in delivering ultra-fast ionic conductors that are compatible with both alkali anodes and high-voltage cathodes. Such a challenging task cannot be fulfilled, without solid understanding covering materials stability and properties, interfacial reactions, structural integrity, and electrochemical windows. Here in this work, we will review recent advances on fundamental modeling in the framework of material genome initiative based on the density functional theory (DFT), focusing on solid alkali batteries. Efforts are made in offering a dependable road chart to formulate competitive materials and construct "better" batteries.展开更多
In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermark...In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology.First,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center.When a copyright dispute requires copyright authentication for the database to be detected.First,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected.Finally,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance.The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database.展开更多
As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more...As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.展开更多
Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity.To solve these problems of com-plexity,time-consuming,and low accuracy in obtaining the fracture tou...Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity.To solve these problems of com-plexity,time-consuming,and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments.A combination prediction model is proposed based on the principle of materials genome engineering,the fracture toughness values of nickel-based superalloys at different temperatures,and different compositions can be predicted based on the existing experimental data.First,to solve the problem of insufficient feature extraction based on manual experience,the Deep Belief Network(DBN)is used to extract features,and an attention mechanism module is introduced.To achieve the purpose of strengthen-ing the important features,an attention weight is assigned to each feature accord-ing to the importance of the feature.Then,the feature vectors obtained by the DBN module based on the Attention mechanism(A-DBN)are spliced with the original features.Thus,the prediction accuracy of the model is improved by extracting high-order combined features and low-order linear features between input and output data.Finally,the spliced feature vectors are put into the Support Vector Regression(SVR)model to further improve the regression prediction abil-ity of the model.The results of the contrast experiment show that the model can effectively improve the prediction accuracy of the fracture toughness value of nickel-based superalloys.展开更多
Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the l...Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.展开更多
Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper,and an introduction to the materials data mining(MDM)process is provided using case studies.Both qualitative and...Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper,and an introduction to the materials data mining(MDM)process is provided using case studies.Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery,design,and optimization.State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co_(3)O_(4) superstructures,materials design of layered double hydroxide,battery materials discovery,and thermoelectric materials design.The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation,and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.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
The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion ...The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion migration,electrode/electrolyte surface/interface,structural(phase)and thermodynamics stability of the electrode materials,physics of intercalation and deintercalation.The relationship between the physical/chemical nature of the LSB materials and the batteries performance is summarized and discussed.A general thread of computational materials design for LSB materials is emphasized concerning all the discussed physics problems.In order to fasten the progress of the new materials discovery and design for the next generation LSB,the Materials Genome Initiative(MGI)for LSB materials is a promising strategy and the related requirements are highlighted.展开更多
The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development an...The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the technologies.Traditional manual methods are time-consuming and laborintensive in screening and optimizing material properties.Materials genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and development.High-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials databases.Therefore,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV technologies.This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.展开更多
基金supported by the National Key R&D Program of China(No.2022YFB3707302)the National Natural Science Foundation of China(Nos.52394271 , 52394270).
文摘Enhancing the mechanical properties is crucial for polyimide films,but the mechanical properties(Young's modulus,tensile strength,and elongation at break)mutually constrain each other,complicating simultaneous enhancement via traditional trial-and-error methods.In this work,we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechani-cal properties.We first established machine learning models to predict Young's modulus,tensile strength,and elongation at break to explore the chemical space containing thousands of candidate structures.The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films.The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations,and the structural rationale was revealed by"gene"analysis and feature importance evaluation.This work provides a cost-effective strategy for designing polyimide films withenhancedmechanical properties.
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This research was supported financially by National Natural Sci-ence Foundation of China(Nos.52130108,51961160729,51871016,11790293,52071024)Guangdong Basic and Applied Basic Research Foundation(Nos.2020B1515120077 and2022A1515110805)+3 种基金the Funds for Creative Research Groups of China(No.51921001)Program for Changjiang Scholars and Innovative Research Team in University of China(No.IRT_14R05)the Fundamental Research Fund for the Central Universities(No.FRF-TP-22-001C2)State Key Lab of Advanced Metals and Materials(No.2022-ZD01).
文摘Metallic glasses(MGs)have attracted extensive attention in the past decades due to their unique chem-ical,physical and mechanical properties promising for a wide range of engineering applications.A thor-ough understanding of their structure-property relationships is the key to the development of novel MGs with desirable performance.New strategies,as proposed by Materials Genome Initiative(MGI),construct a new paradigm for high-throughput materials discovery and design,and are being increas-ingly implemented in the search of new MGs.While a few reports have summarized the application of high-throughput and/or machine learning techniques,a comprehensive assessment of materials genome strategies for developing MGs is still missing.Herein,this paper aims to present a timely overview of key advances in this fascinating subject,as well as current challenges and future opportunities.A holistic approach is used to cover the related topics,including high-throughput preparation and characterization of MGs,and data-driven machine learning strategies for accelerating the development of novel MGs.Fi-nally,future research directions and perspectives for MGI-assisted design of MGs are also proposed and surmised.
基金Project supported by the National Key R&D Program of China(Grant No.2016YFB0700503)the National High Technology Research and Development Program of China(Grant No.2015AA03420)+2 种基金Beijing Municipal Science and Technology Project,China(Grant No.D161100002416001)the National Natural Science Foundation of China(Grant No.51172018)Kennametal Inc
文摘Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.
基金Project supported by the National Natural Science Foundation of China(Grant No.51772321)the Beijing Science and Technology Project(Grant No.D171100005517001)+1 种基金the National Key Research and Development Plan(Grant No.2017YFB0701602)the Youth Innovation Promotion Association(Grant No.2016005)
文摘This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.
基金support by National Science Foundation under award number ACI-1550404American Chemical Society Petroleum Research Fund under the award number 55481-DNI6+1 种基金Global Research Outreach(GRO)Program of Samsung Advanced Institute of Technology under the award number 20164974the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering under the Office of Naval Research grant N00014-16-1-2569
文摘High-throughput computational materials design provides one efficient solution to accelerate the discovery and development of functional materials. Its core concept is to build a large quantum materials repository and to search for target materials with desired properties via appropriate materials descriptors in a high-throughput fashion, which shares the same idea with the materials genome approach. This article reviews recent progress of discovering and developing new functional materials using high-throughput computational materials design approach. Emphasis is placed on the rational design of high-throughput screening procedure and the development of appropriate materials descriptors, concentrating on the electronic and magnetic properties of functional materials for various types of industrial applications in nanoelectronics.
文摘The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.
基金intellectually led by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract No. DE-AC02-05-CH11231 (Materials Project program KC23MP). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DOE-ERCAP0026371. T.W.Ko also acknowledges the support of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures program. We also acknowledged AdvanceSoft Corporation for implementing the LAMMPS interface.
文摘Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-source graph deep learning library for materials science and chemistry.Built on top of the popular Deep Graph Library(DGL)and Python Materials Genomics(Pymatgen)packages,MatGL is designed to be an extensible“batteries-included”library for developing advanced model architectures for materials property predictions and interatomic potentials.At present,MatGL has efficient implementations for both invariant and equivariant graph deep learning models,including the Materials 3-body Graph Network(M3GNet),MatErials Graph Network(MEGNet),Crystal Hamiltonian Graph Network(CHGNet),TensorNet and SO3Net architectures.MatGL also provides several pretrained foundation potentials(FPs)with coverage of the entire periodic table,and property prediction models for out-of-box usage,benchmarking and fine-tuning.Finally,MatGL integrates with PyTorch Lightning to enable efficient model training.
基金financially supported by the 863 Program(No.2015AA020941)the National Natural Science Foundation of China(Nos.21474003 and 91427304)+1 种基金National Science Foundation of USA(Nos.DMR-0906898 and DMR-1408872)the Joint-Hope Education Foundation.W.B.Z.acknowledges support from the National"1000 Plan(Youth)"of China
文摘This essay discusses some preliminary thoughts on the development of a rational and modular approach for molecular design in soft matter engineering and proposes ideas of structural and functional synthons for advanced functional materials. It echoes the Materials Genome Initiative by practicing a tentative retro-functional analysis （RFA） scheme. The importance of hierarchical structures in transferring and amplifying molecular functions into macroscopic properties is recognized and emphasized. According to the role of molecular segments in final materials, there are two types of building blocks： structural synthon and functional synthon. Guided by a specific structure for a desired function, these synthons can be modularly combined in various ways to construct molecular scaffolds. Detailed molecular structures are then deduced, designed and synthesized precisely and modularly. While the assembled structure and property may deviate from the original design, the study may allow further refinement of the molecular design toward the target function, The strategy has been used in the development of soft fullerene materials and other giant molecules. There are a few aspects that are not yet well addressed： （1） function and structure are not fully decoupled and （2） the assembled hierarchical structures are sensitive to secondary interactions and molecular geometries across different length scales. Nevertheless, the RFA approach provides a starting point and an alternative thinking pathway by provoking creativity with considerations from both chemistry and physics. This is particularly useful for engineering soft matters with supramolecular lattice formation, as in giant molecules, where the synthons are relatively independent of each other.
基金supported by the National Natural Science Foundation of China(22103025,51833003,22173030,21975073,and 51621002).
文摘Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing.Polymeric material genome engineering(PMGE)is becoming a vital platform for the intelligent manufacturing of polymeric materials.However,the development of PMGE is still in its infancy,and many issues remain to be addressed.In this perspective,we elaborate on the PMGE concepts,summarize the state-of-the-art research and achievements,and highlight the challenges and prospects in this field.In particular,we focus on property estimation approaches,including property proxy prediction and machine learning prediction of polymer properties.The potential engineering applications of PMGE are discussed,including the fields of advanced composites,polymeric materials for communications,and integrated circuits.
文摘Fast synthesis and screening of materials are vital to the advance of materials science and are an essential component of the Materials Genome Initiative. Here we use copper-oxide superconductors as an example to demonstrate the power of integrating combinatorial molecular beam epitaxy synthesis with high-throughput electric transport measurements. Leveraging this method, we have generated a phase diagram with more than 800 compositions in order to unravel the doping dependence of interface superconductivity. In another application of the same method, we have studied the superconductorto-insulator quantum phase transition with unprecedented accuracy in tuning the chemical doping level.
基金supported in part by the Zhengzhou Materials Genome Institute,the National Natural Science Foundation of China(No.51001091,111174256,91233101,51602094,51602290,11274100)the Fundamental Research Program from the Ministry of Science and Technology of China(no.2014CB931704)
文摘Due to ever-increasing concern about safety issues in using alkali metal ionic batteries, all solid-state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high-performance ASSBs lies in delivering ultra-fast ionic conductors that are compatible with both alkali anodes and high-voltage cathodes. Such a challenging task cannot be fulfilled, without solid understanding covering materials stability and properties, interfacial reactions, structural integrity, and electrochemical windows. Here in this work, we will review recent advances on fundamental modeling in the framework of material genome initiative based on the density functional theory (DFT), focusing on solid alkali batteries. Efforts are made in offering a dependable road chart to formulate competitive materials and construct "better" batteries.
基金This work is supported by Foundation of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research No.ICDDXN004Foundation of Beijing Advanced Innovation Center for Materials Genome Engineering.
文摘In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology.First,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center.When a copyright dispute requires copyright authentication for the database to be detected.First,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected.Finally,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance.The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database.
基金Project supported by the National Key Basic Research Program of China(Grant Nos.2015CB921000,2016YFA0300301,2017YFA0303003,and 2017YFA0302902)the National Natural Science Foundation of China(Grant Nos.11674374,11804378,and 11574372)+3 种基金the Beijing Municipal Science and Technology Project(Grant No.Z161100002116011)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant Nos.QYZDB-SSW-SLH008 and QYZDY-SSW-SLH001)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB07020100)the Opening Project of Wuhan National High Magnetic Field Center(Grant No.PHMFF2015008)
文摘As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.
基金supported by Beijing Advanced Innovation Center for Materials Genome Engineering,Beijing Information Science and Technology University,Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(No.ICDDXN004).
文摘Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity.To solve these problems of com-plexity,time-consuming,and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments.A combination prediction model is proposed based on the principle of materials genome engineering,the fracture toughness values of nickel-based superalloys at different temperatures,and different compositions can be predicted based on the existing experimental data.First,to solve the problem of insufficient feature extraction based on manual experience,the Deep Belief Network(DBN)is used to extract features,and an attention mechanism module is introduced.To achieve the purpose of strengthen-ing the important features,an attention weight is assigned to each feature accord-ing to the importance of the feature.Then,the feature vectors obtained by the DBN module based on the Attention mechanism(A-DBN)are spliced with the original features.Thus,the prediction accuracy of the model is improved by extracting high-order combined features and low-order linear features between input and output data.Finally,the spliced feature vectors are put into the Support Vector Regression(SVR)model to further improve the regression prediction abil-ity of the model.The results of the contrast experiment show that the model can effectively improve the prediction accuracy of the fracture toughness value of nickel-based superalloys.
基金supported by the National Natural Science Foundation of China(51991351,51827803,52103122,and 22375138)the Institutional Research Fund from Sichuan University(no.2021SCUNL201)the Fundamental Research Funds for the Central Universities,and the 111 project(B20001).
文摘Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.
基金Financial supports to this work from National Key Research and Development Program of China(No.2016YFB0700504,2017YFB0701600)Science and Technology Commission of Shanghai Municipality of China(No.15DZ2260300 and No.16DZ2260600)are gratefully acknowledged.
文摘Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper,and an introduction to the materials data mining(MDM)process is provided using case studies.Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery,design,and optimization.State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co_(3)O_(4) superstructures,materials design of layered double hydroxide,battery materials discovery,and thermoelectric materials design.The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation,and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.11234013,11064004 and 11264014)supported by the"Gan-po talent 555"project of Jiangxi Province
文摘The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion migration,electrode/electrolyte surface/interface,structural(phase)and thermodynamics stability of the electrode materials,physics of intercalation and deintercalation.The relationship between the physical/chemical nature of the LSB materials and the batteries performance is summarized and discussed.A general thread of computational materials design for LSB materials is emphasized concerning all the discussed physics problems.In order to fasten the progress of the new materials discovery and design for the next generation LSB,the Materials Genome Initiative(MGI)for LSB materials is a promising strategy and the related requirements are highlighted.
基金the financial support from the National Natural Science Foundation of China(52394273 and 52373179).
文摘The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the technologies.Traditional manual methods are time-consuming and laborintensive in screening and optimizing material properties.Materials genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and development.High-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials databases.Therefore,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV technologies.This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.
