A machine learning platform for the discovery of materials

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