Scientific article

Machine Learning for Analysis of Time-Resolved Luminescence Data

Published inACS Photonics, vol. 5, no. 12, p. 4888-4895
Publication date2018

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 photo- luminescence 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.

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Citation (ISO format)
ĐORĐEVIĆ, Nikola et al. Machine Learning for Analysis of Time-Resolved Luminescence Data. In: ACS Photonics, 2018, vol. 5, n° 12, p. 4888–4895. doi: 10.1021/acsphotonics.8b01047
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Article (Published version)
ISSN of the journal2330-4022

Technical informations

Creation02/25/2019 7:10:00 PM
First validation02/25/2019 7:10:00 PM
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