IEEE PVSC 49
Search
SPLTRAK Abstract Submission
Prediction of Electron Band Gap of A2XY6 Perovskite Compounds using Machine Learning
Jatin Chaudhary1, Swastik Bhattacharya2, Jukka Heikkonen1, Rajeev Kanth 3
1University of Turku, Turku, Finland
/2University of Colorado, Boulder, CO, United States
/3Savonia University of Applied Sciences, KUOPIO, Finland

Increasing population and industrialization have led to an uptick in energy requirements. This has led to the depletion of traditional sources of energy. The exhaustion of these traditional sources has led to power generation from renewable sources, namely wind energy, hydro-power, and solar energy. The wide availability of sunlight and simplicity in converting sunlight to electricity has led to the search for synthesized semiconductors that give high efficiency in this conversion. A family of such semiconductors attains the perovskite structure, the most established being Methyl Ammonium Lead Iodide. The shortcomings of this compound include lead poisoning, motivating the search for perovskite structures that have low electron band-gap and are stable. A family of such perovskite structures is compounds that attain an A2XY6 type structure. This paper demonstrates some methods that can be used to calculate the electron band-gap of such compounds. The metrics found from Support Vector Machine Regression and Random Forest Regression are compared and analyzed to propose a scalable model for predicting electron band-gap.