How Machine Learning Accelerates the Development of Quantum Dots? †-Reference-Cited by-同舟云学术

How Machine Learning Accelerates the Development of Quantum Dots? †

Published:2020-12-23 Issue:1 Volume:39 Page:181-188
ISSN:1001-604X
Container-title:Chinese Journal of Chemistry
language:en
Short-container-title:Chin. J. Chem.

Author:

Peng Jia¹,Muhammad Ramzan¹,Wang Shu‐Liang²,Zhong Hai‐Zheng¹

Affiliation:

1. MIIT Key Laboratory for Low‐Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering Beijing Institute of Technology 100081 Beijing China

2. School of Computer Science & Technology, Beijing Institute of Technology Beijing 100081 China

Publisher

Wiley

Subject

General Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjoc.202000393

Reference72 articles.

1. Machine learning for molecular and materials science

2. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

3. 2011MGI White Paper.https://www.mgi.gov/.

4. The Materials Genome Initiative: One year on

5. The Materials Genome Initiative, the interplay of experiment, theory and computation

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry;Coordination Chemistry Reviews;2024-10

2. Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data;Advanced Intelligent Systems;2024-09-09

3. Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots;International Journal of Nanomedicine;2024-06

4. Deep-learning-based inverse design of colloidal quantum dots;Optics Communications;2024-05

5. Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots;Nature Communications;2024-03-29