Artificial neural network potential for pure zinc

Zinc (Zn), because of its aberrant c/a ratio, has proven difficult to model using classical interatomic potentials such as the Modified Embedded Atom Method (MEAM). The limitations of existing formalisms in modeling Zn have been overcome here using a machine learned interatomic potential. This potential is trained using a database of density functional theory(DFT) calculations generated using the generalized gradient approximation. The resulting feedforward perceptron has a minimal architecture with a single hidden layer of 20 neurons. Validation of the network generated potential demonstrates that the potential correctly reproduced the training database while also predicting the experimentally observed c/a ratio as a function of temperature. The network is able to simultaneously predict the correct c/a ratio while also finding the hexagonally close packed structure as the ground state, which has not been previously demonstrated with semi-emprical potentials. This potential shows various results which are in good agreement with DFT and experimental calculation and will be a useful tool in the simulation of Zn at the molecular dynamics scale.

Automated summary

In this study, a machine learned interatomic potential was used to model Zinc, overcoming the limitations of classical interatomic potentials. Validation of the network generated potential showed accurate reproduction of the training database and correct predictions of experimentally observed phenomena. The potential demonstrated good agreement with DFT and experimental calculations, making it a useful tool for simulating Zinc at the molecular dynamics scale.