Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, deep learning pipelines are notoriously data-hungry, while generating reference calculations is computationally demanding. To overcome this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) in describing chemical environments, together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by a flexible kernel function that incorporates chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches. We make the code available to the community at https://github.com/IsakFalk/atomistic_transfer_mekrr.

本文提出了一种使用图神经网络和核平均嵌入的迁移学习方法，能够通过特征图来学习催化过程的系统特异性数据，以及通过一个灵活的核函数来融合化学物种信息，能够有效地提高性能和可解释性，同时具有出色的推广和传递性能。

使用GNN和核均值嵌入的原子模拟转移学习