Although structures of the inorganic core of CdS atomically precise quantum dots were reported, characterizing the nature of the metal-carboxylate coordination in these materials remains a challenge due to the large number of possible isomers. The computational cost imposed by first-principles methods is prohibitive for such a configurational search, and empirical potentials are not available. In this work, we applied deep neural network algorithms to train a potential for CdS clusters with carboxylate ligands using a database of energies and gradients obtained from density functional theory calculations. The derived potential provided energies and gradients based on a set of reference structures. Our trained potential was then used to accelerate genetic algorithm and molecular dynamics simulations searches of low-energy structures, which in turn, were used to compute the X-ray diffraction and electronic absorption spectra. Our results for CdS clusters with carboxylate ligands, analyzed and compared with experimental findings, demonstrated that the structure of a cluster whose properties agree better with experiment may deviate from the one previously assumed.