31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga2Ox via Supervised Machine Learning Models.

This work presents machine learning (ML) assisted Fermi level prediction of solution‐processed ultra‐wide bandgap (UWB) amorphous gallium oxide (a‐Ga2Ox) which can significantly accelerate the fabrication of semiconducting UWB a‐Ga2Ox‐based material for future display application. Different models such as Kernel Ridge Regression (KRR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were trained with empirical features, including experimental thickness, annealing temperature and environment during the solution‐processed UWB a‐Ga2Ox film fabrication. This work is a big step towards rapid and cost‐effective optimization method of fabricating UWB a‐Ga2Ox‐based devices. [ABSTRACT FROM AUTHOR]