Research Projects

Researchers are focusing on exploring high-performance electronic, thermoelectric, and optoelectronic materials to improve global energy management. They are specifically interested in studying carrier transport properties of new energy materials MYZ using advanced machine learning and artificial intelligence algorithms. Traditional methods require a complete electron-phonon matrix element to estimate intrinsic carrier mobility in semiconductors, which is computationally expensive. Deformation potential theory (DPT) is used to estimate intrinsic carrier mobility and phonon thermal conductivity, considering parameters such as carrier effective mass, elastic modulus, and strain-induced shifts of band edges. Fortran codes have been written to solve Boltzmann transport equations within DPT to estimate Seebeck coefficients, relaxation time for electron and hole, charge carrier mobility, electrical conductivity, relaxation times for phonons, thermal conductivity, and compare results with BoltzTraP code. This method has been successfully used to compute transport properties for oxide and chalcogenides. To study optoelectronic properties, non-diabetic molecular dynamics (NAMD) study is used to accurately predict excited state properties, implemented in the PYXAID code. Recent works on lead halide perovskites, which exhibit solar cell applications, are also being published. The researchers plan to work on predicting new advanced materials with better transistor, thermoelectric, and optoelectronic efficiency using high-throughput approaches. They are also developing machine learning models to predict carrier transport parameters by analyzing the impact of appropriate descriptors and algorithms, such as SVR, KRR, and XGBoost.