Npj Comput. Mater.: 玻璃结构预测—懂物理的机器学习
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氧化物玻璃可以通过改变成分来调整物理性能以满足各种应用的需求，但可能的虚拟样本庞大，远超出实验室制备和表征的数量级。物理模型，可以提供对玻璃结构的洞察并准确推断简单体系，但有可能过度简化复杂体系中的问题。机器学习（ML）模型，可以准确预测用于模型训练的成分空间内的玻璃结构，但难以准确预测范围之外的结构。要解决这一挑战，就需要一种组合模型，通过将玻璃成分如何影响短程原子结构的物理知识，嵌入机器学习，以提高模型对氧化物玻璃结构的预测和外推能力。
该文近期发表于npj Computational Materials 8：192(2022)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Predicting glass structure by physics-informed machine learning
Mikkel L. Bødker, Mathieu Bauchy, Tao Du, John C. Mauro & Morten M. Smedskjaer
Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na2O–SiO2 glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training.
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