用图神经网络与高斯过程结合，高效找出分子吸附位点。
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites
- 用图神经网络+高斯过程构建可量化不确定性的预测模型。
- 在复杂分子上以更少迭代次数找到最优吸附位置。
- 无需人工特征，适用于任意分子且可跨尺寸泛化。
本文提出一种新型灵活优化流程AUGUR（Aware of Uncertainty Graph Unit Regression），用于确定最优吸附位点。该模型结合图神经网络与高斯过程，构建具有不确定性量化能力的预测器，具备对称性、平移和旋转不变性。该预测器作为代理模型，驱动数据高效的贝叶斯优化，从而确定大而复杂的簇的最优吸附位置。该流程相比现有最先进方法所需迭代次数显著减少。模型不依赖手工特征，可无缝应用于任意分子且无需修改；图结构的池化特性使同一模型能处理不同大小分子，实现由小分子训练模型来预测计算成本高昂体系的能量。
原文摘要 · Abstract (English)
In this paper, we propose a novel flexible optimization pipeline for determining the optimal adsorption sites, named AUGUR (Aware of Uncertainty Graph Unit Regression). Our model combines graph neural networks and Gaussian processes to create a flexible, efficient, symmetry-aware, translation, and rotation-invariant predictor with inbuilt uncertainty quantification. This predictor is then used as a surrogate for a data-efficient Bayesian Optimization scheme to determine the optimal adsorption positions. This pipeline determines the optimal position of large and complicated clusters with far fewer iterations than current state-of-the-art approaches. Further, it does not rely on hand-crafted features and can be seamlessly employed on any molecule without any alterations. Additionally, the pooling properties of graphs allow for the processing of molecules of different sizes by the same model. This allows the energy prediction of computationally demanding systems by a model trained on comparatively smaller and less expensive ones
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。