用图神经网络实现原子存在度连续化，可优化材料表面氧化过程
Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
- 引入原子存在度连续变量，扩展图神经网络消息传递机制
- 首次实现对吉布斯自由能关于原子坐标与存在度的联合求导
- 适用于复杂表面氧化体系的高效结构优化，适合材料设计研究者
机器学习势能已成为材料科学中不可或缺的工具，可研究更大体系和更长时间尺度。当前先进模型多为图神经网络，通过消息传递迭代更新原子嵌入以预测性质。本文在消息传递框架中引入连续变量来表征原子存在度的分数值，从而可计算吉布斯自由能对原子笛卡尔坐标及存在度的梯度。基于此，我们提出一种基于梯度的广义系综优化方法，并在Cu(110)表面氧化体系上验证其有效性。
原文摘要 · Abstract (English)
Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models are generally graph neural networks that employ message passing to iteratively update atomic embeddings that are ultimately used for predicting properties. In this work we extend the message passing formalism with the inclusion of a continuous variable that accounts for fractional atomic existence. This allows us to calculate the gradient of the Gibbs free energy with respect to both the Cartesian coordinates of atoms and their existence. Using this we propose a gradient-based grand canonical optimization method and document its capabilities for a Cu(110) surface oxide.
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