Systematic Study of the Properties of CdS Clusters with Carboxylate Ligands Using a Deep Neural Network Potential Developed with Data from Density Functional Theory Calculations

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.