2020/11/30 by Sutanay Choudhury, Choudhury, Sutanay, Jenna A. Bilbrey +14
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #History and advancements in chemistry #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics #cs.LG #physics.chem-ph
paper · pdf · doi:10.48550/arxiv.2012.00131
Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS)
arxiv created 2020/11/30 · openalex publication_date 2020/11/30 · arxiv updated 2020/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.