Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials.

ABSTRACT: Hybrid organic-inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI₃ perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI₃ crystals, with their respective energies and atomic forces obtain

SUBMITTER: Chen HA 

PROVIDER: S-EPMC6648651 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature