论文标题
基于机器学习的筛查光伏应用的无铅卤化双钙壶
Machine-learning Based Screening of Lead-free Halide Double Perovskites for Photovoltaic Applications
论文作者
论文摘要
无铅卤化双钙晶是光伏领域中碘化甲基铵铅碘化物的有前途的稳定和无毒的替代品。在这种情况下，最常用的双钙钛矿是CS $ _2 $ agbibr $ _6 $，这是由于其有利的充电运输属性。但是，由于其较大的间接间隙及其内在和外部缺陷，该材料获得的最大功率转换效率不超过3 \％。另一方面，在该结构的4个晶格位置替代不同元素的材料空间很大，但仍未探索。在这项工作中，神经网络用于从7056个结构的初始空间中预测双钙壶的带隙，并选择适合可见光吸收的候选者。连续的混合DFT计算用于评估热力学稳定性，功率转化效率和所选化合物的有效质量，并提出新型的潜在太阳吸收器。
Lead-free halide double perovskites are promising stable and non-toxic alternatives to methylammonium lead iodide in the field of photovoltaics. In this context, the most commonly used double perovskite is Cs$_2$AgBiBr$_6$, due to its favorable charge transport properties. However, the maximum power conversion efficiency obtained for this material does not exceed 3\%, as a consequence of its wide indirect gap and its intrinsic and extrinsic defects. On the other hand, the materials space that arises from the substitution of different elements in the 4 lattice sites of this structure is large and still mostly unexplored. In this work a neural network is used to predict the band gap of double perovskites from an initial space of 7056 structures and select candidates suitable for visible light absorption. Successive hybrid DFT calculations are used to evaluate the thermodynamic stability, the power conversion efficiency and the effective masses of the selected compounds, and to propose novel potential solar absorbers.
