data mining-aided materials discovery and optimization
Clicks: 143
ID: 170916
2017
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 | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.