论文标题
非平衡分子传递的快速和不确定性方向信息
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
论文作者
论文摘要
化学的许多重要任务围绕反应过程中的分子旋转。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近平衡状态上。在本文中，我们旨在通过三种方式扩展此范围。首先，我们提出了Dimenet ++模型，该模型比平衡分子的QM9基准上的原始Dimenet快8倍，精度高10％。其次，我们通过开发挑战性的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畸形构型。最后，我们研究了不确定性定量的连续和均值估计，以加速探索非平衡结构的广阔空间。我们的Dimenet ++实现以及Coll数据集可在线获得。
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.
