Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics

TL;DR

This paper introduces a novel electron-informed molecular representation learning method that enhances the understanding of real-world molecular physics without extra computational costs, achieving state-of-the-art results.

Contribution

It proposes a transfer-based approach to incorporate electron-level information into large molecules, overcoming computational barriers of direct electron data acquisition.

Findings

Achieved state-of-the-art accuracy on benchmark datasets

Effectively transfers electron information from small to large molecules

Improves molecular physics predictions without additional computation

Abstract

Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL.