ABSTRACT We propose a novel descriptor of materials, named ‘cation fingerprints’, based on the chemical formula or concentrations of raw materials and their respective properties. To test its performance, this method was used to predict the viscosity of glass materials using the experimental database INTERGLAD. Using artificial neural network models, we succeeded in predicting the temperature required for glass to have a specific viscosity within a root-mean-square error of 33.0°C. We were also able to evaluate the effect of particular target raw materials using a model trained without including the specific target raw material. The results show that cation fingerprints with a neural network model can predict some unseen combinations of raw materials. In addition, we propose a method for estimating the prediction accuracy by calculating cosine similarity of the input features of the material which we want to predict.

中文翻译：

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使用人工神经网络的阳离子指纹预测氧化物玻璃材料的粘度行为

摘要 我们根据原材料的化学式或浓度及其各自的特性，提出了一种新的材料描述符，称为“阳离子指纹”。为了测试其性能，该方法用于使用实验数据库 INTERGLAD 预测玻璃材料的粘度。使用人工神经网络模型，我们成功地预测了玻璃具有特定粘度所需的温度，均方根误差为 33.0°C。我们还能够使用不包括特定目标原材料的训练模型来评估特定目标原材料的效果。结果表明，带有神经网络模型的阳离子指纹可以预测一些看不见的原材料组合。此外，