data mining-aided materials discovery and optimization

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ID: 170916
2017
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Abstract
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.
Reference Key
lu2017journaldata Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wencong Lu;Ruijuan Xiao;Jiong Yang;Hong Li;Wenqing Zhang
Journal The Journal of antimicrobial chemotherapy
Year 2017
DOI
10.1016/j.jmat.2017.08.003
URL
http://www.sciencedirect.com/science/article/pii/S2352847817300618
Keywords
Data mining layered double hydroxide thermoelectric materials materials design mechanics of materials materials genome initiativematerials of engineering and construction

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