EDBench: Large-Scale Electron Density Data for Molecular Modeling

TL;DR

EDBench is a large-scale, high-quality dataset of electron density data for molecules, enabling improved machine learning models to understand electronic properties efficiently, with broad applications in molecular science.

Contribution

The paper introduces EDBench, the first large-scale electron density dataset with benchmark tasks, facilitating ML research in electronic structure understanding and reducing reliance on costly DFT calculations.

Findings

Models trained on EDBench achieve high accuracy in electron density prediction.

Learning-based methods can approximate ED with significantly less computational cost.

EDBench enables new applications in drug discovery and materials science.

Abstract

Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in accurately understanding molecular force fields (MFFs). ED describes the probability of finding electrons at specific locations around atoms or molecules, which uniquely determines all ground state properties (such as energy, molecular structure, etc.) of interactive multi-particle systems according to the Hohenberg-Kohn theorem. However, the calculation of ED relies on the time-consuming first-principles density functional theory (DFT) which leads to the lack of large-scale ED data and limits its application in MLFFs. In this paper, we introduce EDBench, a large-scale, high-quality dataset of ED designed to advance learning-based research at the electronic scale. Built upon the PCQM4Mv2, EDBench provides accurate ED data, covering 3.3 million molecules. To comprehensively evaluate the ability of models to understand and utilize electronic information, we design a suite of ED-centric benchmark tasks spanning prediction, retrieval, and generation. Our evaluation on several state-of-the-art methods demonstrates that learning from EDBench is not only feasible but also achieves high accuracy. Moreover, we show that learning-based method can efficiently calculate ED with comparable precision while significantly reducing the computational cost relative to traditional DFT calculations. All data and benchmarks from EDBench will be freely available, laying a robust foundation for ED-driven drug discovery and materials science.