Data mining-aided materials discovery and optimization

Data mining-aided materials discovery and optimization

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...

Full description

Bibliographic Details
Main Authors: Wencong Lu, Ruijuan Xiao, Jiong Yang, Hong Li, Wenqing Zhang
Format: Article
Language:English
Published: Elsevier 2017-09-01
Series:Journal of Materiomics
Subjects:
Data mining
Materials design
Co3O4 superstructures
Layered double hydroxide
Battery materials
Thermoelectric materials
Materials genome initiative
Online Access:http://www.sciencedirect.com/science/article/pii/S2352847817300618
id doaj-9cb47daf556243a8bc2a2178426fab4a
record_format Article
spelling doaj-9cb47daf556243a8bc2a2178426fab4a2020-11-25T01:05:12ZengElsevierJournal of Materiomics2352-84782017-09-013319120110.1016/j.jmat.2017.08.003Data mining-aided materials discovery and optimizationWencong Lu0Ruijuan Xiao1Jiong Yang2Hong Li3Wenqing Zhang4Materials Genome Institute of Shanghai University, Shanghai Materials Genome Institute, Shanghai 200444, ChinaInstitute of Physics, Chinese Academy of Sciences, Beijing, ChinaMaterials Genome Institute of Shanghai University, Shanghai Materials Genome Institute, Shanghai 200444, ChinaInstitute of Physics, Chinese Academy of Sciences, Beijing, ChinaMaterials Genome Institute of Shanghai University, Shanghai Materials Genome Institute, Shanghai 200444, ChinaRecent 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 Co3O4 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.http://www.sciencedirect.com/science/article/pii/S2352847817300618Data miningMaterials designCo3O4 superstructuresLayered double hydroxideBattery materialsThermoelectric materialsMaterials genome initiative
collection DOAJ
language English
format Article
sources DOAJ
author Wencong Lu
Ruijuan Xiao
Jiong Yang
Hong Li
Wenqing Zhang
spellingShingle Wencong Lu
Ruijuan Xiao
Jiong Yang
Hong Li
Wenqing Zhang
Data mining-aided materials discovery and optimization
Journal of Materiomics
Data mining
Materials design
Co3O4 superstructures
Layered double hydroxide
Battery materials
Thermoelectric materials
Materials genome initiative
author_facet Wencong Lu
Ruijuan Xiao
Jiong Yang
Hong Li
Wenqing Zhang
author_sort Wencong Lu
title Data mining-aided materials discovery and optimization
title_short Data mining-aided materials discovery and optimization
title_full Data mining-aided materials discovery and optimization
title_fullStr Data mining-aided materials discovery and optimization
title_full_unstemmed Data mining-aided materials discovery and optimization
title_sort data mining-aided materials discovery and optimization
publisher Elsevier
series Journal of Materiomics
issn 2352-8478
publishDate 2017-09-01
description 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 Co3O4 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.
topic Data mining
Materials design
Co3O4 superstructures
Layered double hydroxide
Battery materials
Thermoelectric materials
Materials genome initiative
url http://www.sciencedirect.com/science/article/pii/S2352847817300618
work_keys_str_mv AT wenconglu dataminingaidedmaterialsdiscoveryandoptimization
AT ruijuanxiao dataminingaidedmaterialsdiscoveryandoptimization
AT jiongyang dataminingaidedmaterialsdiscoveryandoptimization
AT hongli dataminingaidedmaterialsdiscoveryandoptimization
AT wenqingzhang dataminingaidedmaterialsdiscoveryandoptimization
_version_ 1725195661891076096

Similar Items