Estimation of Electronic and Optical Properties of Chalcopyrite Semiconductors Using Machine Learning

In this paper, we have predicted the electronic and optical properties of AIBIIIC2VI chalcopyrite semiconductors using Ada-Boost algorithm under the framework of machine learning. The values of bandgap \((E_{g} )\), refractive index (n), bulk modulus (B) and dielectric constant \((\varepsilon )\) of AIBIIIC2VI chalcopyrite semiconductors are predicted employing lattice constants, bond length, ionicity \((f_{i} )\) and plasmon energy \((\hbar \omega_{p} )\) as input parameters by means of Ada-Boost algorithm. The results obtained were compared with available experimental and theoretical values. The result presented are found to be in good agreement with experimental values and having less average percentage deviation compared to reported theoretical values.