Machine learning methods. We used the neural network module as implemented in scikit-learn and performed a Bayesian optimization based hyperparameter optimization. The hyperparameters included the number of hidden layers, the size of the first hidden layer, the ratio between subsequent hidden layer sizes, the non-linear activation function, the L2 regularization parameter, the learning rate as well as the patience of the learning rate scheduler. The latter reduced the learning rate with a factor of 0.5. We used the adam optimization algorithm to train the model. The best performing model had one hidden layer with 125 neurons, an L2 regularization parameter of 10.53, a tanh activation function and a patience of the learning rate scheduler of 17 and an initial learning rate of 2.6•10 -4 . The conformation of the PEDOT oligomers consisted of all inverse bond lengths (short range distances), all inverse distances between sulfur and oxygen atoms (long range distances), the eigenvalues of the Coulomb matrix, the eigenvalues of modified Coulomb matrices using electronegativities (H: 2.2, O: 3.44, C: 2.55, S: 2.58) and ones instead of the atomic numbers Z.