本技术说明描述了Graphormer的最新更新，包括架构设计修改以及对3D分子动力学模拟的适应。通过这些简单的修改，Graphormer可以在大规模分子建模数据集上获得比vanilla更好的结果，并且可以在2D和3D分子图建模任务上一致地获得性能增益。此外，我们还表明，通过全局感受野和自适应聚合策略，Graphormer比经典的基于消息传递的GNN更强大。根据经验，Graphormer可以实现比KDD Cup 2021中使用的PCQM 4 M量子化学数据集上最初报告的结果少得多的MAE。与此同时，它在最近的开放式催化剂挑战赛中大大超过了竞争对手，这是NeurIPS 2021研讨会上的竞赛赛道，旨在用先进的人工智能模型对催化剂-吸附物反应系统进行建模。所有代码都可以在https://github.com/Microsoft/Graphormer上找到。
英文摘要：
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple modifications, Graphormer could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on 2D and 3D molecular graph modeling 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. Empirically, Graphormer could achieve much less MAE than the originally reported results on the PCQM4M quantum chemistry dataset used in KDD Cup 2021. In the meanwhile, it 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 codes could be found at https://github.com/Microsoft/Graphormer.
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