To achieve this, we consider a logarithmic transform of the selectivity instead of the raw value because the goal is rather to predict the order of magnitude of the selectivity values than to directly predict the higher values of selectivity -an ML model that focuses its prediction on raw selectivity values can reach lower errors by simply focusing on the higher values than the lower ones. The use of a logarithmic transform better separates the different selectivity categories through the different orders of magnitude of the selectivity values. This approach distributes more evenly the efforts on the whole spectrum of selectivity values. Moreover, this logarithmic transformation is effectively an exchange Gibbs free energy (defined later in equation 1), so that we can easily compare it with the energy descriptors introduced in this article.