Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
MSU Affiliation
James Worth Bagley College of Engineering; Dave C. Swalm School of Chemical Engineering; Center for Computational Sciences
Research Mentor
Neeraj Rai
Creation Date
7-25-2025
Abstract
Formamidinium lead bromide (FAPbBr3) perovskite crystals display promising optoelectronic properties, making them attractive materials for solar cells, light-emitting diodes, and photoelectrochemical devices. In this study, computational methods are applied to investigate the effects of point defects—vacancies, interstitials, Frenkel, and Schottky—on the electronic structure of FAPbBr3 bulk and nanoplatelet crystals. Traditional computational methods, such as density functional theory (DFT), are computationally expensive and thus limited to small systems and short time scales. To characterize and simulate larger systems over longer time periods at reduced computational costs, machine learned interatomic potentials (MLIPs) are developed with MACE, a message passing neural network. The machine learning model is trained on ab initio molecular dynamics (AIMD) simulations of defect-free and defectpresent systems to run molecular dynamics (MD) simulations. Several models are trained using different combinations of AIMD systems data, with the model trained on defect-free bulk FAPbBr3 systems being the most accurate. The MACE MD simulations provide key insights into the change in band structure and density of states of FAPbBr3 crystals with defects present. This study illustrates the effectiveness of MLIPs in capturing defect-driven behavior in FAPDBr3, enabling accurate, large-scale MD simulations and informing the more efficient use of FAPbBr3 and intelligent design of perovskites in optoelectronic applications.
Presentation Date
Summer 7-31-2025
Recommended Citation
Lee, Rachel; Lane, John Michael; Wilson, Woodrow; and Rai, Neeraj, "Investigating the Defect Behavior and Electronic Properties of Formamidinium Lead Bromide Perovskite through Machine Learned Interatomic Potentials" (2025). Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science. 15.
https://scholarsjunction.msstate.edu/ccs-reu/15
