论文标题
大规模分子建模数据集的图形师的经验研究
An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets
论文作者
论文摘要
该技术说明描述了图形配置器的最新更新，包括体系结构设计修改以及对3D分子动力学仿真的适应。 “ GraphorMer-V2”可以比香草一家在大规模分子建模数据集上获得更好的结果，并且在下游任务上可以始终获得性能增长。此外，我们表明，借助全球接收场和自适应聚合策略，Graphormer比经典的基于消息的GNN更强大。 Graphormer-V2的MAE比在KDD Cup 2021中使用的PCQM4M量子化学数据集上的Vanilla Graphormer少得多，后者在本次比赛中赢得了第一名。同时，Graphormer-V2在最近的开放式催化剂挑战中大大优于竞争对手，该挑战是Neurips 2021研讨会上的竞赛轨道，旨在模拟使用高级AI模型的Catalyst-Adsorbate反应系统。所有模型均可在\ url {https://github.com/microsoft/graphormer}上找到。
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. The "Graphormer-V2" could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on downstream tasks. In addition, we show that with a global receptive field and an adaptive aggregation strategy, Graphormer is more powerful than classic message-passing-based GNNs. Graphormer-V2 achieves much less MAE than the vanilla Graphormer on the PCQM4M quantum chemistry dataset used in KDD Cup 2021, where the latter one won the first place in this competition. In the meanwhile, Graphormer-V2 greatly outperforms the competitors in the recent Open Catalyst Challenge, which is a competition track on NeurIPS 2021 workshop, and aims to model the catalyst-adsorbate reaction system with advanced AI models. All models could be found at \url{https://github.com/Microsoft/Graphormer}.
