this was achieved by passing the voltage responses as inputs to three machine learning algorithms: dense neural networks (DNNs), one-dimensional convolutional neural networks (1D-CNNs), and support vector machines (SVMs) using linear and radial basis function (RBF) kernel functions. DNNs were used as they can model complex, non-linear relationships in highdimensional data, although they can be prone to overfitting when data is limited. The DNN was built using TensorFlow 2.15.0 , in Python 3.10.2 according to the structure outlined in Table and Figure . 1D-CNNs, on the other hand, are well-suited for capturing spatial patterns in sequential data, such as those found in the voltage responses, potentially reducing the chance of overfitting. The 1D-CNN was also built using TensorFlow according to the specifications in Table and Figure . SVMs were included for their memory efficiency via their calculation of optimal decision hyperplanes however, their performance can degrade with highly non-linear data. The SVM models were implemented using scikit-learn 1.2.2 , in Python 3.10.2. Additional details regarding the architecture of these algorithms are provided in the supplementary information. Table . Properties of each layer of the 1-dimensional convolutional neural network. C = convolutional layer, MP = max pool layer, D = dense layer. The output of the convolutional layers is flattened before it can be passed into the fully connected layer since it has a higher dimensionality from the number of filters used.