期刊文献+
共找到10篇文章
< 1 >
每页显示 20 50 100
The materials data ecosystem: Materials data science and its role in data-driven materials discovery 被引量:2
1
作者 Hai-Qing Yin Xue Jiang +4 位作者 Guo-Quan Liu Sharon Elder Bin Xu Qing-Jun Zheng Xuan-Hui Qu 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第11期120-125,共6页
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
关键词 materials Genome initiative materials data science data classification life-cycle curation
下载PDF
Discovery and design of lithium battery materials via high-throughput modeling 被引量:1
2
作者 Xuelong Wang Ruljuan Xiao +1 位作者 Hong Li Llquan Chen 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第12期27-34,共8页
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
关键词 materials Genome initiative lithium battery materials high-throughput simulations material design
下载PDF
Ab initio artificial intelligence:Future research of Materials Genome Initiative 被引量:3
3
作者 He Li Yong Xu Wenhui Duan 《Materials Genome Engineering Advances》 2023年第2期25-30,共6页
The marriage of artificial intelligence(AI)and Materials Genome Initiative(MGI)could profoundly change the landscape of modern materials research,leading to a new paradigm of data-driven and AI-driven materials discov... The marriage of artificial intelligence(AI)and Materials Genome Initiative(MGI)could profoundly change the landscape of modern materials research,leading to a new paradigm of data-driven and AI-driven materials discovery.In this perspective,we will give an overview on the central role of AI in the MGI research.In particular,an emerging research field of ab initio AI,which applies state-of-the-art AI techniques to help solve bottleneck problems of ab initio computation,will be introduced.The development of ab initio AI will greatly accelerate highthroughput computation,promote the construction of large materials database,and open new opportunities for future research of MGI. 展开更多
关键词 ab initio methods artificial intelligence materials Genome initiative
原文传递
Machine learning in materials genome initiative:A review 被引量:21
4
作者 Yingli Liu Chen Niu +4 位作者 Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2020年第22期113-122,共10页
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
关键词 materials genome initiative(MGI) materials database Machine learning materials properties prediction materials design and discovery
原文传递
Combinatorial synthesis and high-throughput characterization of copper-oxide superconductors 被引量:1
5
作者 J Wu A T Bollinger +1 位作者 X He I Bozovic 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第11期126-129,共4页
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
关键词 materials Genome initiative combinatorial growth high-throughput characterization copperoxide superconductors
下载PDF
Materials genome strategy for metallic glasses 被引量:1
6
作者 Zhichao Lu Yibo Zhang +6 位作者 Wenyue Li Jinyue Wang Xiongjun Liu Yuan Wu Hui Wang Dong Ma Zhaoping Lu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2023年第35期173-199,共27页
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
关键词 Metallic glasses materials genome initiative High-throughput techniques Machine learning
原文传递
High-throughput research on superconductivity
7
作者 Mingyang Qin Zefeng Lin +3 位作者 Zhongxu Wei Beiyi Zhu Jie Yuan Kui Jin 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第12期9-15,共7页
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
关键词 SUPERCONDUCTIVITY materials genome initiative high-throughput experimental technology high-throughput research mode
下载PDF
Data mining-aided materials discovery and optimization 被引量:14
8
作者 Wencong Lu Ruijuan Xiao +2 位作者 Jiong Yang Hong Li Wenqing Zhang 《Journal of Materiomics》 SCIE EI 2017年第3期191-201,共11页
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
关键词 Data mining materials design Co3O4 superstructures Layered double hydroxide Battery materials Thermoelectric materials materials genome initiative
原文传递
Physics towards next generation Li secondary batteries materials:A short review from computational materials design perspective 被引量:6
9
作者 OUYANG ChuYing CHEN LiQuan 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS 2013年第12期2278-2292,共15页
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
关键词 lithium secondary batteries physics problems computational materials design materials genome initiative
原文传递
How Machine Learning Accelerates the Development of Quantum Dots?
10
作者 Jia Peng Ramzan Muhammad +1 位作者 Shu-Liang Wang Hai-Zheng Zhong 《Chinese Journal of Chemistry》 SCIE CAS CSCD 2021年第1期181-188,共8页
With the rapid developments in the field of information technology,the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and proc... With the rapid developments in the field of information technology,the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and process de-velopment.Machine learning emerges as a new research paradigm to accel-erate the application-oriented material discovery.Quantum dots are ex-panded as functional nanomaterials to enhance cutting-edge photonic technology.However,they suffer from uncertainty in industrial fabrication and application.Here,we discuss how machine learning accelerates the development of quantum dots.The basic principles and operation proce-dures of machine learning are described with a few representative examples of quantum dots.We emphasize how machine learning contributes to the optimization of synthesis and the analysis of material characterizations.To conclude,we give a short perspective discussing the problems of combining machine learning and quantum dots. 展开更多
关键词 Quantum dots Machine learning materials genome initiative Neural networks ON-DEMAND
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部