Abstract
Robust solutions combining computational chemistry and data-driven approaches are in high demand in various areas of materials science. For instance, such methods can use machine learning models trained on a limited dataset to make structure-to-property predictions over large search spaces. This paper examines the impact of data selection mechanisms on thermodynamic property assessments for chemically modified lead halide perovskite γ-CsPbI(3) and non-perovskite δ-CsPbI(3). For disordered states of these phases, complete composition/configuration spaces are built by adding Cd or Zn substitutions of Pb and Br substitutions of I and comprise 2,946,709 and 2,995,462 inequivalent spatial arrangements of substituents, respectively. Using the properties of 1162 entries of the built spaces evaluated by means of density functional theory, we implement independent procedures for training graph neural networks (GNNs). In each of them, a training dataset is constructed depending on the defect contents and presence of low- and high-symmetry structures. The results show that symmetries of training structures can significantly influence quality of the subsequent GNNs' predictions and can result in twofold increase in errors due to the preferential selection of high-symmetry structures.