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.

中文翻译：

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用于发现材料的机器学习平台

对于光伏材料，带隙 $$E_{g}$$ 等属性是材料是否适合执行所需功能的关键指标。计算 $$E_{g}$$ 通常使用密度泛函理论 (DFT) 方法执行，但使用诸如 GW 近似等方法执行更准确的计算。通常用于计算电子特性的 DFT 软件包括 VASP、CRYSTAL、CASTEP 或 Quantum Espresso 等应用程序。根据材料的晶胞大小和对称性，这些计算在计算上可能很昂贵。在这项研究中，我们提出了一个新的机器学习平台，用于准确预测各种材料的属性，例如 $$E_{g}$$。