Machine learning in materials science
Beijing University of Posts and Telecommunications · Institute of Semiconductors · +3 more institutions
Abstract
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning…
Citation impact
- FWCI
- 22.48
- Percentile
- 100%
- References
- 153
Authors
8- JWJing Wei
Beijing University of Posts and Telecommunications
- XCXuan Chu
Beijing University of Posts and Telecommunications
- XSXiangyu Sun
Beijing University of Posts and Telecommunications
- KXKun Xu
Beijing University of Posts and Telecommunications
- HDHui‐Xiong Deng
Institute of Semiconductors, University of Chinese Academy of Sciences
Topics & keywords
- Pace
- Computer science
- Machine learning
- Artificial intelligence
- Development (topology)
- Ranging
- Industrial engineering
- Engineering
Funding
- NNNational Natural Science Foundation of ChinaAwards: 61622406, 61571415, XDB30000000, 61622406 61571415, 2016YFB0700700
- CAChinese Academy of SciencesAwards: XDB30000000, 2017YFA0207500, 2016YFB0700700
- CPChina Postdoctoral Science FoundationAward: 2017M620694
- NPNational Postdoctoral Program for Innovative TalentsAward: BX201700040
- NKNational Key Research and Development Program of ChinaAwards: 2016YFB0700700, XDB30000000, 2017YFA0207500