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.
@article{arxiv.2012.00131,
title = {HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data},
author = {Sutanay Choudhury and Jenna A. Bilbrey and Logan Ward and Sotiris S. Xantheas and Ian Foster and Joseph P. Heindel and Ben Blaiszik and Marcus E. Schwarting},
journal= {arXiv preprint arXiv:2012.00131},
year = {2020}
}
Comments
Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS)