Abstract Deep convolution neural networks (ConvNets) have been recently used to predict the enthalpy of formation and the prediction errors are within DFT precision. Here we show that a multi-channel input for the ConvNets improves the prediction accuracy, and the accuracy can be further improved by decomposing the input signals into high/low frequencies. We trained ConvNets using the periodic table representation on a DFT formation enthalpy dataset of 10,590 elpasolite compounds. The mean absolute error (MAE) reaches 50 meV/atom, which is half value of the MAE of a ConvNet using single input channel. The dependence of MAE on each element was also analyzed. Our work demonstrates the importance of input data preprocessing for ConvNets prediction accuracy in material informatics tasks.

中文翻译：

---

用于材料特性预测的多通道卷积神经网络

摘要 深度卷积神经网络 (ConvNets) 最近已被用于预测形成焓，并且预测误差在 DFT 精度范围内。在这里，我们展示了 ConvNets 的多通道输入提高了预测精度，并且可以通过将输入信号分解为高频/低频来进一步提高精度。我们在包含 10,590 种 elpasolite 化合物的 DFT 形成焓数据集上使用元素周期表表示训练了 ConvNets。平均绝对误差 (MAE) 达到 50 meV/atom，这是使用单输入通道的 ConvNet 的 MAE 值的一半。还分析了 MAE 对每个元素的依赖性。我们的工作证明了输入数据预处理对于材料信息学任务中 ConvNets 预测准确性的重要性。