论文标题
Hydronet：用于保存分子数据中分子间相互作用和结构基序的基准任务
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
论文作者
论文摘要
分子间和远程相互作用是现象的核心，如基因调节，量子材料的拓扑状态，电池中的电解质传输以及水的通用溶剂化特性。我们通过使用最近发表的495万个水簇的数据集，通过氢键相互作用并导致较长的结构模式来保留化学问题的机器学习方法中的分子间相互作用和结构基序的一组挑战问题。该数据集提供空间坐标以及两种类型的图表，以适应各种机器学习实践。
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water. We present a set of challenge problems for preserving intermolecular interactions and structural motifs in machine-learning approaches to chemical problems, through the use of a recently published dataset of 4.95 million water clusters held together by hydrogen bonding interactions and resulting in longer range structural patterns. The dataset provides spatial coordinates as well as two types of graph representations, to accommodate a variety of machine-learning practices.
