Work

HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data

Year: 2020

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2012.00131

Authors: Choudhury, Sutanay, Bilbrey, Jenna A., Ward, Logan, Xantheas, Sotiris S., Foster, Ian, Heindel, Joseph P., Blaiszik, Ben, Schwarting, Marcus E.

Institutions: Pacific Northwest National Laboratory, Argonne National Laboratory, University of Washington, University of Chicago

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Battelle, Office of Science, Argonne National Laboratory, Pacific Northwest National Laboratory

Awards: G102163711, G1313983767, G1664757510, G2287611682, G262962599, G2994398150, G2998698001, G3075337988, Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure, G498139845, G513797252, G6848031779, G6918803902, G969889393, G993149817

OpenAlex ID: W3106862072

Cites: 30

Cited by: 2

Related to: 10