This article describes a neural network system for predicting the secondary electron yield of metallic materials. For bulk metals, experimental values are used as training data. Due to the strong correlation between the secondary electron yield and the work function, deep learning predicts the secondary electron yield with relatively high accuracy even with a small amount of training data. Our approach demonstrates the importance of the work function in predicting the secondary electron yield. For the secondary electron yield of thin metal films on metal substrates, deep learning predictions are generated using training data obtained by Monte Carlo simulations. The accuracy of the secondary yield predictions of thin films on substrates could be improved by adding experimental values of bulk metals to the training data.

中文翻译：

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使用深度学习预测金属材料的二次电子产额。

本文介绍了一种用于预测金属材料二次电子产额的神经网络系统。对于大块金属，实验值用作训练数据。由于二次电子产额与功函数之间的强相关性，深度学习即使在少量训练数据的情况下也能以较高的精度预测二次电子产额。我们的方法证明了功函数在预测二次电子产率中的重要性。对于金属基板上金属薄膜的二次电子产率，使用蒙特卡罗模拟获得的训练数据生成深度学习预测。通过将大块金属的实验值添加到训练数据中，可以提高基板上薄膜二次产量预测的准确性。