We introduce GlassMLP, a machine learning framework using physics-inspired structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to atomistic models in 2D and 3D. Its performance is better than the state of the art while being more parsimonious in terms of training data and fitting parameters. GlassMLP quantitatively predicts four-point dynamic correlations and the geometry of dynamic heterogeneity. Transferability across system sizes allows us to efficiently probe the temperature evolution of spatial dynamic correlations, revealing a profound change with temperature in the geometry of rearranging regions.

中文翻译：

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通过物理启发的机器学习预测玻璃形成液体中的动态异质性

我们引入 GlassMLP，这是一种机器学习框架，使用受物理启发的结构输入来预测深度​​过冷液体中的长期动力学。我们将这种深度神经网络应用于 2D 和 3D 原子模型。它的性能优于现有技术，同时在训练数据和拟合参数方面更加简洁。GlassMLP 定量预测四点动态相关性和动态异质性的几何形状。跨系统尺寸的可转移性使我们能够有效地探测空间动态相关性的温度演变，揭示重新排列区域的几何形状随温度的深刻变化。