Recent Advances
How Machine Learning Accelerates the Development of Quantum Dots?†
Jia Peng,
Ramzan Muhammad,
Shu-Liang Wang,
Hai-Zheng Zhong,
Jia Peng
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
Search for more papers by this authorRamzan Muhammad
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
Search for more papers by this authorShu-Liang Wang
School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081 China
Search for more papers by this authorCorresponding Author
Hai-Zheng Zhong
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
E-mail: [email protected]Search for more papers by this authorJia Peng,
Ramzan Muhammad,
Shu-Liang Wang,
Hai-Zheng Zhong,
Jia Peng
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
Search for more papers by this authorRamzan Muhammad
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
Search for more papers by this authorShu-Liang Wang
School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081 China
Search for more papers by this authorCorresponding Author
Hai-Zheng Zhong
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, 100081 Beijing, China
E-mail: [email protected]Search for more papers by this author† Dedicated to the 80th Anniversary of Beijing Institute of Technology.
Abstract
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 development. Machine learning emerges as a new research paradigm to accelerate the application-oriented material discovery. Quantum dots are expanded 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 procedures 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.
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