A quantum chemistry dataset containing ground-state and conical-intersection structures of 260k molecules

TL;DR

This paper presents a large quantum chemistry dataset of 260,000 molecules, including ground-state and conical-intersection structures, to facilitate machine learning in photochemistry.

Contribution

The creation of a comprehensive dataset of ground-state and conical-intersection structures for small molecules at multiple quantum chemistry levels is a novel resource for the field.

Findings

Dataset includes optimized geometries and energies at OM2 and OM2/MRCI levels.

Enables integration of photochemistry insights with machine learning approaches.

Bridges the gap between experimental data and data-driven modeling in photochemistry.

Abstract

Conical intersections play central roles in photoinduced reactions. However, comprehensive conical-intersection datasets that could advance our understanding of excited-state reaction processes remain scarce. To address this gap, we constructed a quantum chemistry dataset containing ground-state and conical-intersection structures of small molecules (up to ten heavy atoms: C, N, O, F). Ground-state geometries were optimized at the semi-empirical OM2 level, with single-point energies calculated at the OM2/MRCI level. Conical-intersection geometries and energies were also computed at the OM2/MRCI level. This dataset is designed to enable a deep integration of photochemistry with machine learning, bridging the gap between photochemical insight and data-driven approaches.