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
Predicting and controlling the microstructure evolution within a material are considered as the‘‘holy grail’’of materials science and engineering.Many important engineering materials are designed by controlling th...Predicting and controlling the microstructure evolution within a material are considered as the‘‘holy grail’’of materials science and engineering.Many important engineering materials are designed by controlling their phase transformations and microstructure evolution.Examples include the improvement of mechanical properties through solid-state precipitation reactions in Nibased superalloys and age-hardened Al alloys,the useful dielectric properties and electro-mechanical coupling effects by manipulating the phase transitions in ferroelectric crystals,and the memory effect of shape-memory alloys by utilizing martensitic transformations.Phase-field method has become the method of choice for modeling three-dimensional microstructure evolution for a wide variety of materials processes.This short article briefly discusses the potential roles that phase-field method can play in the Materials Genome Initiative.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
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
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
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supported by the National Science Foundation through the Designing Materials to Revolutionize and Engineer our Future(DMREF)Initiative(DMR-1234096)the Grant Opportunities for Academic Liaison with Industry(GOALI)Program(CMMI-1235092)
文摘Predicting and controlling the microstructure evolution within a material are considered as the‘‘holy grail’’of materials science and engineering.Many important engineering materials are designed by controlling their phase transformations and microstructure evolution.Examples include the improvement of mechanical properties through solid-state precipitation reactions in Nibased superalloys and age-hardened Al alloys,the useful dielectric properties and electro-mechanical coupling effects by manipulating the phase transitions in ferroelectric crystals,and the memory effect of shape-memory alloys by utilizing martensitic transformations.Phase-field method has become the method of choice for modeling three-dimensional microstructure evolution for a wide variety of materials processes.This short article briefly discusses the potential roles that phase-field method can play in the Materials Genome Initiative.
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.
