A quantum chemistry dataset containing S0-S1 conical-intersection structures of 259k molecules
Abstract
Conical intersections are key to photoinduced reactions, but comprehensive datasets of their structures remain rare. To address this gap, we built the QCDGE-CI dataset, which contains ground-state and minimum-energy conical-intersection structures for more than 259k small molecules with up to ten heavy atoms (C, N, O, or F). The minimum-energy conical-intersection geometries were computed using the semi-empirical OM2/MRCI method and evaluated through systematic benchmarks with an enlarged active space and CASSCF calculations. These benchmarks support the reliability of the geometries. Cheminformatics analyses further reveal the broad chemical diversity covered by the dataset. The dataset is designed to support machine-learning and data-driven studies of excited-state chemistry, including nonadiabatic processes.
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BibTeX
@misc{zhang2026quantum,
title = {{A quantum chemistry dataset containing S0-S1 conical-intersection structures of 259k molecules}},
author = {Jiahui Zhang and Yifei Zhu and Chuqiao Feng and Yingjin Ma and Chao Xu and Zhenggang Lan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.14287},
eprint = {2605.14287},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.14287},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A quantum chemistry dataset containing S0-S1 conical-intersection structures of 259k molecules
AU - Jiahui Zhang
AU - Yifei Zhu
AU - Chuqiao Feng
AU - Yingjin Ma
AU - Chao Xu
AU - Zhenggang Lan
PY - 2026
DA - 2026/09/20
PB - arXiv
DO - 10.48550/arxiv.2605.14287
UR - https://arxiv.org/abs/2605.14287
AB - Conical intersections are key to photoinduced reactions, but comprehensive datasets of their structures remain rare. To address this gap, we built the QCDGE-CI dataset, which contains ground-state and minimum-energy conical-intersection structures for more than 259k small molecules with up to ten heavy atoms (C, N, O, or F). The minimum-energy conical-intersection geometries were computed using the semi-empirical OM2/MRCI method and evaluated through systematic benchmarks with an enlarged active space and CASSCF calculations. These benchmarks support the reliability of the geometries. Cheminformatics analyses further reveal the broad chemical diversity covered by the dataset. The dataset is designed to support machine-learning and data-driven studies of excited-state chemistry, including nonadiabatic processes.
ER -