标题： 基于图神经网络的分数原子存在性梯度优化 显示英文标题

标题： Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence

摘要： 机器学习原子间势能已成为材料科学中不可或缺的工具，使研究更大系统和更长时间尺度成为可能。 最先进的模型通常是图神经网络，它们使用信息传递来迭代更新原子嵌入，这些嵌入最终用于预测性质。 在本工作中，我们通过引入一个连续变量来扩展信息传递形式，该变量考虑了分数原子存在性。 这使我们能够计算吉布斯自由能相对于原子笛卡尔坐标及其存在性的梯度。 利用这一点，我们提出了一种基于梯度的巨正则优化方法，并记录了其在Cu(110)表面氧化物中的能力。 显示英文摘要

摘要： 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.