Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online.

中文翻译：

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非平衡分子的快速且不确定性的定向消息传递

在化学过程中，许多重要的任务围绕着分子展开。这需要远离平衡的预测，而分子机器学习的最新工作集中在平衡或接近平衡的状态。本文旨在通过三种方式扩展此范围。首先，我们提出了DimeNet ++模型，该模型比平衡分子QM9基准上的原始DimeNet快8倍，准确度高10％。其次，我们通过开发具有挑战性的COLL数据集来验证高反应性分子上的DimeNet ++，该数据集包含碰撞过程中小分子的扭曲构型。最后，我们研究用于不确定性量化的集合和均方差估计，目的是加速探索非平衡结构的广阔空间。