Prediction of Defect Structure in MoS\({}_{\mathbf{2}}\) by Given Properties

Abstract

The generation of crystals with tailored properties is a significant challenge in both scientific research and practical applications. Due to the vast configuration space of crystalline structures, finding precise solutions to such problems is computationally intensive. In this study, we propose a method for generating defect configurations in MoS\({}_{2}\) crystals aimed at producing crystals with specific characteristics, focusing on formation energy and HOMO-LUMO energy levels as key examples. The approach leverages symbolic regression techniques, trained on datasets of two-dimensional materials with defects, to predict crystal properties. We introduce methods for identifying defect configurations with both minimal and specific formation energies, as well as for optimizing HOMO-LUMO energy levels. The main advantages of this approach are its efficiency and accuracy in generating valid and optimized crystal structures.