Mapping materials science problems onto computational frameworks suitable for machine learning can accelerate materials discovery. Combining proposed crystal site feature embedding (CSFE) representation with convolutional and extensive deep neural networks, we achieve a low mean absolute test error of 3.7 meV/atom and 0.069 eV on density functional theory energies and band gaps of mixed halide perovskites. We explore how a small amount of cadmium doping can potentially be applied in solar cell design and sample the large chemical space by using a variational autoencoder to discover interesting perovskites with band gaps in the ultraviolet and infrared. Additionally, we use CSFE to explore chemical spaces and small doping concentrations beyond those used for training. We further show that CSFE has a mean absolute test error of 7 meV/atom and 0.13 eV for total energies and band gaps for 2D perovskites and discuss its adaptability for exploration of an even wider variety of chemical systems.

将材料科学问题映射到适合机器学习的计算框架上可以加速材料发现。将提出的晶体位点特征嵌入（CSFE）表示与卷积和广泛的深层神经网络相结合，我们在混合泛卤化钙钛矿的密度泛函理论能量和带隙方面实现了3.7 meV /原子和0.069 eV的低平均绝对测试误差。我们探索了如何在太阳能电池设计中潜在地应用少量镉掺杂，并通过使用变分自编码器来发现在紫外和红外带隙中有趣的钙钛矿，从而对大型化学空间进行采样。另外，我们使用CSFE来探索化学空间和超出训练所用的少量掺杂浓度。我们进一步证明CSFE的平均绝对测试误差为7 meV / atom和0。