## 4 Conclusions  
We aimed to replace the conventional expert intuition-driven process of developing new electrolyte additives with a deep learning-based approach through this study. This research enables the proposal of a more diverse range of molecular candidates, including those that may be challenging for human researchers to explore, while predicting their HOMO and LUMO properties. It is evident that HOMO and LUMO are important factors in electrolyte additive design, but they are not the only determining elements. However, simulating the chemical reactions and interactions among various molecules remains an extremely complex task and continues to be a significant challenge for many researchers [66]. Therefore, as a practical and widely recognized approach, we focused on generating new candidate molecules based on their HOMO and LUMO characteristics [67; 68; 69]. To obtain the HOMO and LUMO values of a molecule, DFT calculations must be performed, which require a significant amount of time. However, through this study, we confirmed that the trained model not only accurately predicts the HOMO and LUMO values of the electrolyte additive dataset but also demonstrates strong predictive performance for the HOMO and LUMO values of newly generated molecules (see Table 3). Therefore, domain experts can leverage generative models to explore novel molecular structures for electrolyte additives. This enables the design and synthesis of new electrolyte additives based on the predicted HOMO and LUMO values while eliminating the need for computationally expensive DFT calculations in the initial screening process. This suggests that the proposed approach has the potential to accelerate the efficient development and design of electrolyte additives [70; 71].  
Conceptualization, J.L.; Methodology, D.Y.; Software, D.Y.; Validation, D.Y. and J.L.; Writing--original draft, D.Y.; Writing--review & editing, J.L.; Supervision, S.L.; Project administration, J.L. and S.L.; Funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Test Molecules** & **Property** & **MAE (eV)** \\ \hline \multirow{4}{*}{Unimol} & \multirow{2}{*}{Electrolyte} & \multirow{2}{*}{1000} & HOMO & 0.22385 \\ \cline{3-5}  & & & LUMO & 0.04176 \\ \cline{2-5}  & & & HOMO & 0.10706 \\ \cline{2-5}  & \multirow{2}{*}{*Generated} & \multirow{2}{*}{110} & LUMO & 0.16052 \\ \cline{3-5}  & & & HOMO & 0.02099 \\ \cline{3-5}  & & & LUMO & 0.00158 \\ \cline{2-5}  & \multirow{2}{*}{*Generated} & \multirow{2}{*}{1060} & HOMO & 0.04996 \\ \cline{3-5}  & & & LUMO & 0.06895 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Molecular property prediction accuracy.  
**Funding:** This work was funded by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. RS-2023-00221723, Development of AI simulator for electrolyte development for improving performance and solving problems of next-generation secondary battery).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets from the Materials Project [[https://next-gen.materialsproject.org](https://next-gen.materialsproject.org)]([https://next-gen.materialsproject.org](https://next-gen.materialsproject.org)), accessed on 13 March 2025, and the Electrolyte Genome Project [[https://www.jcesr.org/research/electrolyte-genome/](https://www.jcesr.org/research/electrolyte-genome/)]([https://www.jcesr.org/research/electrolyte-genome/](https://www.jcesr.org/research/electrolyte-genome/)), accessed on 13 March 2025, were used in this study. The additional private dataset generated during the study is not publicly available due to institutional restrictions but may be made available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.