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This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python.
The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.
The Volumetric Camera Calibration Dataset is used for a camera calibration system. Intersecting laser beams are traversed over a volume in the test domain. At each location, the intersecting beams are imaged by camera1 and camera2. A test object is imaged for evaluation.
This dataset is created for the paper titled 'Co-benefits of Transport Demand Reductions from Compact Urban Development in Chinese Cities' and published on Nature Sustainability. We construct 6 scenarios of compact urban development, alternative energy vehicle deployment, and power decarbonization to explore the co-benefits of transport demand reductions via compact urban development for carbon emissions, energy use, air quality, and human health in China in 2050. This dataset provides the following gridded information for the scenarios: (1) monthly mean surface PM2.5 concentrations from the WRF-Chem model; (2) annual PM2.5-related premature deaths calculated by the GEMM model; (3) 2015 population in China; (4) mask for provinces in China; (5) longitude and latitude of each grid center.
