TY - JOUR AU - Dordevic, Nikola AU - Beckwith, Joseph S. AU - Yarema, Maksym AU - Yarema, Olesya AU - Rosspeintner, Arnulf AU - Yazdani, Nuri AU - Leuthold, Juerg AU - Vauthey, Eric AU - Wood, Vanessa TI - Machine Learning for Analysis of Time-Resolved Luminescence Data PY - 2018 JF - ACS Photonics JA - ACS Photonics VL - 5 IS - 12 SP - 4888 EP - 4895 L1 - https://pubs.acs.org/doi/pdf/10.1021/acsphotonics.8b01047 L2 - https://pubs.acs.org/doi/10.1021/acsphotonics.8b01047 L3 - http://pubs.acs.org/doi/10.1021/acsphotonics.8b01047 L4 - http://www.unige.ch/sciences/chifi/publis/pics/double/ref01635.png M3 - 10.1021/acsphotonics.8b01047 UR - http://dx.doi.org/10.1021/acsphotonics.8b01047 N2 - 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. ID - 1635 ER -