期刊
ACS PHOTONICS
卷 5, 期 12, 页码 4888-4895出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsphotonics.8b01047
关键词
machine learning; luminescence; radiative rate; quantum emitters; nanocrystals
类别
资金
- ETH Research Grant [ETH-45 14-2]
- Swiss National Science Foundation through the Quantum Sciences and Technology NCCR
- Swiss National Science Foundation [200020-165890]
- [200021175889]
- Swiss National Science Foundation (SNF) [200020_165890] Funding Source: Swiss National Science Foundation (SNF)
Time-resolved photoluminescence is one of the most standard techniques to understand and systematically optimize the performance of optical materials and optoelectronic devices. Here, we present a machine learning code to analyze time-resolved photoluminescence data and determine the decay rate distribution of an arbitrary emitter without any a priori assumptions. To demonstrate and validate our approach, we analyze computer-generated time-resolved photoluminescence data sets and show its benefits for studying the photoluminescence of novel semiconductor nanocrystals (quantum dots), where it quickly provides insight into the possible physical mechanisms of luminescence without the need for educated guessing and fitting.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据