A machine learning platform for the discovery of materials

Abstract For photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating is often performed using Density Functional Theory (DFT) methods, although more accurate calculation methods GW approximation. DFT software used compute electronic includes applications VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on unit cell size and symmetry material, these calculations can be computationally expensive. In this study, we present new machine learning platform for prediction wide range materials.