  Machine-learned potentials (MLPs) trained on ab initio data combine the
computational efficiency of classical interatomic potentials with the accuracy
and generality of the first-principles method used in the creation of the
respective training set. In this work, we implement and train a MLP to obtain
an accurate description of the potential energy surface and property
predictions for organic compounds, as both single molecules and in the
condensed phase. We devise a dual descriptor, based on the atomic cluster
expansion (ACE), that couples an information-rich short-range description with
a coarser long-range description that captures weak intermolecular
interactions. We employ uncertainty-guided active learning for the training set
generation, creating a dataset that is comparatively small for the breadth of
application and consists of alcohols, alkanes, and an adipate. Utilizing that
MLP, we calculate densities of those systems of varying chain lengths as a
function of temperature, obtaining a discrepancy of less than 4% compared with
experiment. Vibrational frequencies calculated with the MLP have a root mean
square error of less than 1 THz compared to DFT. The heat capacities of
condensed systems are within 11% of experimental findings, which is strong
evidence that the dual descriptor provides an accurate framework for the
prediction of both short-range intramolecular and long-range intermolecular
interactions.
