For photovoltaic materials, properties such as band gap $$E_{g}$$ are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ 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 study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ of a wide range of materials.
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