HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
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2020-11
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通讯作者：
Sutanay Choudhury;Jenna A. Bilbrey;Logan T. Ward;S. Xantheas;Ian T Foster;Josef Heindel;B. Blaiszik
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作者：
Sutanay Choudhury;Jenna A. Bilbrey;Logan T. Ward;S. Xantheas;Ian T Foster;Josef Heindel;B. Blaiszik
分子间和长程相互作用是基因调控、量子材料的拓扑状态、电池中的电解质传输以及水的普遍溶剂化性质等多种现象的核心。我们提出了一组挑战性的问题，以保持分子间的相互作用和结构基序的机器学习方法来解决化学问题，通过使用最近发表的数据集的495万水簇通过氢键相互作用结合在一起，并导致更长的范围内的结构模式。该数据集提供空间坐标以及两种类型的图形表示，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.