Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT > 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.

中文翻译：

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利用融合轨道场矩阵和成分描述符的深度卷积神经网络进行高通量热电材料筛选

热电材料收集废热并将其转化为可重复使用的电力。热电材料还广泛应用于冰箱和冷却电子设备等相反领域。然而，迄今为止最流行和已知的热电材料都是凭直觉提出和发现的，主要是通过实验。不幸的是，通过试错实验来合成和测量热电​​特性非常耗时和资源消耗。在这里，我们开发了一种卷积神经网络（CNN）分类模型，利用融合轨道场矩阵和成分描述符来筛选大量材料，以发现功率因数高于 10 μW/cm K2 的新热电候选材料。该模型使用我们自己的高通量密度泛函理论计算生成的数据，结合从头算散射和输运包，在不假设电子弛豫时间恒定的情况下获得电子输运性质，这确保了比以前的研究更可靠的电子输运性质计算。该分类模型还与一些传统的机器学习算法（例如梯度提升和随机森林）进行了比较。我们在从开放量子材料数据库筛选出的具有非零带隙的 3465 个立方动态稳定结构上部署了分类模型。我们鉴定出许多ZT>2的高性能热电材料。在 300 至 700 K 的宽温度范围内，对于不同掺杂浓度的 n 型和 p 型掺杂，该值均为 1 或接近 1。此外，我们的特征重要性和最大信息系数分析证明了两个以前未报道的材料描述符，即平均熔化温度和电负性的低平均偏差，它们与功率因数密切相关，从而为快速筛选潜在热电材料提供了一条成功的新途径速度。我们的深度 CNN 模型具有融合轨道场矩阵和成分描述符，对于从大规模假设结构中筛选高功率因数热电材料非常有前景。