Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911356365570048 |
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| author | Fu, Li Lv, Longfei Zhang, Fan Zhou, Si Gao, Weiwei Zhao, Jijun |
| author_facet | Fu, Li Lv, Longfei Zhang, Fan Zhou, Si Gao, Weiwei Zhao, Jijun |
| contents | Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of SF-active molecules. Trained on a previously reported FORMED database, the GNN achieves state-of-the-art accuracy in predicting SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with Time-Dependent Density Functional Theory validation by four orders of magnitude and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The approach presented herein exemplifies an effective strategy for accelerating the discovery of functional molecules with optoelectronic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02661 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks Fu, Li Lv, Longfei Zhang, Fan Zhou, Si Gao, Weiwei Zhao, Jijun Materials Science Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of SF-active molecules. Trained on a previously reported FORMED database, the GNN achieves state-of-the-art accuracy in predicting SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with Time-Dependent Density Functional Theory validation by four orders of magnitude and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The approach presented herein exemplifies an effective strategy for accelerating the discovery of functional molecules with optoelectronic applications. |
| title | Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks |
| topic | Materials Science |
| url | https://arxiv.org/abs/2601.02661 |