用廉价结构指纹预训练图神经网络，加速材料发现
Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
- 以低成本结构指纹为目标进行GNN预训练
- 在多种结构描述符上表现接近现有方法
- 适合构建大规模原子数据基础模型的研究者
近年来，预训练图神经网络（GNN）被开发为通用模型，可有效微调用于材料科学中的各类下游任务，并显著提升准确率与数据效率。当前主流预训练方法包括监督学习拟合通用力场或自监督学习通过去噪原子结构平衡。但这些方法均需基于量子力学计算生成的数据集，随着数据规模扩大，计算成本迅速不可行。本文提出一种新预训练目标：使用廉价计算的结构指纹作为目标，同时在多种结构描述符上保持相当性能。实验表明，该方法可作为预训练GNN的通用策略，适用于大规模原子数据基础模型的构建。
原文摘要 · Abstract (English)
In recent years, pre-trained graph neural networks (GNNs) have been developed as general models which can be effectively fine-tuned for various potential downstream tasks in materials science, and have shown significant improvements in accuracy and data efficiency. The most widely used pre-training methods currently involve either supervised training to fit a general force field or self-supervised training by denoising atomic structures equilibrium. Both methods require datasets generated from quantum mechanical calculations, which quickly become intractable when scaling to larger datasets. Here we propose a novel pre-training objective which instead uses cheaply-computed structural fingerprints as targets while maintaining comparable performance across a range of different structural descriptors. Our experiments show this approach can act as a general strategy for pre-training GNNs with application towards large scale foundational models for atomistic data.
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