In addition to developing GPT models, convolutional neural networks (CNN) and gradient boosting regression tree (GBR) models were developed to predict lattice lengths given XRD patterns, with the same train-test split as for GPT models. For the GBR and CNN models, the XRD signals are used as inputs and the three lattice constants as outputs. For GBR, we used 1000 estimators, a learning rate of 0.01, and a maximum depth of 3 with a mean absolute error loss function. The CNN model used in this study, referred to as CNNRegressor, is designed to perform regression tasks by extracting features from one-dimensional input data. The architecture begins with two 1D convolutional layers: the first layer has 16 filters and the second layer has 32 filters, both with a kernel size of 3 and padding of 1 to preserve the input size. Each convolutional layer is followed by a Rectified Linear Unit (ReLU) activation function to introduce non-linearity. MaxPooling layers with a kernel size of 2 and stride of 2 are applied to downsample the feature maps, reducing dimensionality and computational load. After these operations, the output is flattened to a shape of 32 × 45, which feeds into a fully connected layer with 64 neurons. The final output layer contains 3 neurons, corresponding to the three target values predicted by the model. This architecture allows the network to efficiently learn relevant features from the input data for accurate regression. The CNN model was trained for 50 epochs with a batch size of 32.