CdTe nanoparticles as temperature sensors via machine learning of optical properties

We have investigated using CdTe nanoparticles as non-invasive temperature sensors. Optical photoluminescence (PL) spectra and time-resolved photoluminescence (TRPL) were measured as functions of temperature and used as inputs to an artificial neural network (ANN) for purposes of machine learning. Two regimes were studied: low temperature, data taken from 10-320 K in steps of 10 K; and high temperature, data taken from 325-346 K in steps of 1 K. Five data sets were withheld for validation from the low temperature data; four from the high temperature. We used preprocessing techniques of min-max normalization and (for the low temperature regime) interpolation to generate additional training samples. Best results for both regimes were obtained using a seven layer fully connected ANN architecture. Hyperparameters varied to optimize the network include number and size of layers (including convolutional layers), batch normalization, activation functions, learning rates, and dividing the PL and TRPL data into separate input branches. Using a typical 80-20 training/testing split, the low temperature (high temperature) network was able to be trained to 0.1 K (1.0 K) training error and 0.3 K (2.5 K) testing error, which results in an error on the withheld validation data of 3.4 K (5.5 K).