论文标题
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
Predicting dynamic heterogeneity in glass-forming liquids by physics-inspired machine learning
论文作者
论文摘要
我们介绍了使用物理启发的结构输入的机器学习框架GlassMLP，以预测深层冷冷液体中的长期动力学。我们将此深层神经网络应用于2D和3D的原子模型。它的性能优于艺术状态，同时在培训数据和拟合参数方面更加简约。 GlassMLP定量预测四点动态相关性和动态异质性的几何形状。跨系统尺寸的可传递性使我们能够有效探测空间动态相关性的温度演化，从而揭示了重新排列区域的几何形状的深刻变化。
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.
