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.  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. 
Density Functional Theory
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.  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. 
Deep Neural Network