To reduce the computational cost of this method, we employed a machine learning based approach in which we trained a neural network on 80% of the 2048 PEDOT conformers (last snapshots of 32 boxes with 64 PEDOT molecules each) that we analysed using DFT to obtain the energy levels shown in Figure . To represent the conformation of the PEDOT molecules, we used the inverse bond lengths, inverse distances between all sulfur and oxygen atoms, all bond angles as well as the eigenvalues of the Coulomb matrix and a modified Coulomb matrix based on the electronegativities of the elements. We compared a linear regression model to neural networks, a gradient boosting regression model, a random forest model and a Gaussian Process model. Among all methods, a neural network obtained using Bayesian seconds for DFT, each on a single CPU). This makes the generation of the required input vectors for the neural network from 3D coordinates the computational bottleneck.