Data was fitted via multivariate linear regression, which is a combination of multiple linear regressions on independent variables for one dependant variable. A popular variant of leastsquares regression to avoid overfitting is the Ridge regression method. Ridge regression considers that various features might display collinearity. In this instance, the ordinary least squares method is modified to minimise the squared absolute sum of the coefficients, known as so-called L2 regularisation. This is done by introducing a tuning parameter, λ, in the cost function (see supporting information). Similar to ridge regressions, LASSO regression can be employed. In LASSO regression, the cost function minimises the absolute sum of coefficients instead, known as L1 regularisation. As a result, LASSO regression assists to reduce overfitting and can be used in feature selection and hence for physical interpretation a posteriori. λ is a scalar variant, and thus requires continuous data to be standardised for both ridge and lasso regression. When λ increases, the bias increases and the variance decreases. Bias hereby describes how well a model matches a training set, whereas variance describes how much a model changes when it is trained with a different training set. High bias infers a weak match with the training set, while low bias indicates a very close match. High variance means that a model is flexible and prone to overfitting. Low variance means that a model is robust and will not change should any part of the training set be altered. Thus, low bias and low variance is generally preferred. For an unbiased estimate of the model performance, the final model fit would use an average of all the final predictions of each monomer (Scheme 1). In this way, bias is minimised because all the data is being used for training the model n-1 times, and variance is minimised since all the data is being used in the validation set once. As a result, such leave one out cross validation (LOOCV) method allows to compare predictions of the target value as a function of its experimental value.