Ruddlesden–Popper (R–P) phase layered chalcogenide perovskites had attracted broad interest as potential lead-free high-performance photovoltaic absorbers. Ca3Sn2S7 is a graphene-like RP phase perovskite with a ultrahigh carrier mobility and a more significant absorption coefficient in the visible light region than those of the classic hybrid halide perovskite MAPbI3. However, the ultra-low direct bandgap of Ca3Sn2S7 is unfavorable for the photovoltaic application. In this work, we addressed these issues by designing an anion-mixed RP phase perovskite with an appropriate direct bandgap. The idea was to adjust its bandgap with different O proportions from 7.14% to 35.71%. We considered more than 3000 derivative structures of Ca6Sn4S14−xOx (x = 1–5) that were related to the arrangement of mixed S/O atoms. To ensure that the computational models were based on the screened optimal structures, we found that Ca6Sn4S14−xOx (x = 4 and 5) could increase the bandgap of Ca3Sn2S7 into the range of 1.19 eV–1.64 eV and 1.02 eV–1.47 eV, respectively. Meanwhile, Ca6Sn4S14−xOx also had absorption coefficients beyond 105 cm−1. These results made them possible candidates as new-generation photovoltaic absorbers. We also trained the supervised graph convolutional network and the unsupervised Mat-generative adversarial networks (GAN) for accelerating the density functional theory (DFT) calculation of over 3000 structures. Even if considering the time to generate the training samples by DFT, we prove that the Mat-GAN strategy could reduce the DFT calculation consumption by more than 99%. In order to reveal the distributive characteristics of the arrangement of mixed S/O, we adopted active machine learning to analyze the differences of these structures. We found that the O atom would preferentially replace the S in the Sn–S–Sn position.

中文翻译：

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使用机器学习加速设计光伏 Ruddlesden-Popper 钙钛矿 Ca6Sn4S14−xOx

Ruddlesden-Popper (R-P) 相层状硫族化物钙钛矿作为潜在的无铅高性能光伏吸收剂引起了广泛的兴趣。Ca3Sn2S7 是一种类似石墨烯的 RP 相钙钛矿，与经典的杂化卤化物钙钛矿 MAPbI3 相比，具有超高的载流子迁移率和在可见光区域的更显着的吸收系数。然而，Ca3Sn2S7 的超低直接带隙不利于光伏应用。在这项工作中，我们通过设计具有适当直接带隙的阴离子混合 RP 相钙钛矿来解决这些问题。这个想法是将其带隙从 7.14% 调整到 35.71%。我们考虑了超过 3000 种与混合 S/O 原子排列相关的 Ca6Sn4S14−xOx (x = 1-5) 的衍生结构。为了确保计算模型基于筛选出的最佳结构，我们发现 Ca6Sn4S14−xOx（x = 4 和 5）可以将 Ca3Sn2S7 的带隙分别增加到 1.19 eV–1.64 eV 和 1.02 eV–1.47 eV . 同时，Ca6Sn4S14-xOx 的吸收系数也超过 105 cm-1。这些结果使它们成为新一代光伏吸收体的候选者。我们还训练了有监督的图卷积网络和无监督的 Mat 生成对抗网络 (GAN)，以加速超过 3000 个结构的密度泛函理论 (DFT) 计算。即使考虑到 DFT 生成训练样本的时间，我们证明 Mat-G​​AN 策略可以将 DFT 计算消耗减少 99% 以上。为了揭示混合S/O排列的分布特征，我们采用主动机器学习来分析这些结构的差异。我们发现 O 原子会优先取代 Sn-S-Sn 位置的 S。