Gashmard, Hassan; Shakeripour, Hamideh

Lattice energy prediction of ionic crystals using machine learning approach

(2024) 31 Jan., The 6^th Iranian Conference on Computational Physics, Iran. IUT.

One of the effective methods to determine the properties of materials is to use simulation and computational approaches. In recent years, the impressive results of machine learning-based approaches have convinced researchers to solve their problems in this way. The lattice energy of ionic crystals plays an important role in thermodynamic analysis as well as predicting the stability and reactivity of ionic crystals. In this study, using the XGBoost algorithm, we achieved good accuracy in order to calculate and predict the lattice energy of binary ion crystals.

 Keywords: Lattice energy of ionic crystals, XGBoost algorithm, Kapustinskii equation