Chemistry and materials · Paper · June 2021
Identifying Lead-free Perovskites Using Machine Learning for High Efficiency Solar Cells
One of the biggest threats our planet faces is the threat of global warming. Electrical power has become crucial to our modern lifestyle, but today, electricity is generated by burning fossil fuels and coal, which has many harms and disadvantages associated with it. Fortunately, there are multiple alternative energy sources, including sunlight, a powerful, inexhaustible, and clean resource. There are several different types of solar cells, but one of the most efficient and low-cost types is the perovskite solar cell. Perovskite solar cells (PSCs) have recently received considerable attention due to the high energy conversion efficiency achieved within a few years of their inception. However, today, the most common perovskite is methyl ammonium iodide (MAPbI3), which contains levels of toxic lead. The science community has been searching for lower-toxicity perovskite-type materials, but testing all of the possible lead-free perovskites requires a huge amount of time and funding. Recent advances in computing power have enabled the generation of large datasets for materials and data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is the primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. The goal of this project is to create a Machine Learning (ML) driven software system that increases the efficiency of the solar cell design process. I will do this by identifying the best perovskites by optimizing material composition and determining the importance of the features of each element in the perovskite to the overall efficiency of the PSC. The Machine Learning (ML) driven software system needs to accurately predict key information such as the heat of formation (delta Hf) and band gap (Eg) and accurately use the training data to form an accurate prediction of the best materials to form a perovskite solar cell (Im). The models must predict with 90% accuracy of prediction for the project to be successful. This program is written in Python, and uses Machine Learning to make predictions about the heat of formation and bandgap of various double halide perovskites.