## 3 Results
### Results and Experimental Setup
#### 3.2.1 Reconstruction  
To assess the effectiveness of the Variational Autoencoder model in capturing the latent representations of electrolyte additives, we evaluated its reconstruction accuracy. Figures 4 and 5 visualize the latent space of the trained model using the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction method. Each point in the scatter plot represents a molecular sample, where its latent vector, learned through  
Figure 3: Histograms showing the HOMO/LUMO distributions of the QM9 dataset and the Custom Electrolyte dataset. HOMO histogram (**left**) and LUMO histogram (**right**).  
the VAE, is mapped into a lower-dimensional space. The color bar on the right side of the figure represents the learned molecular property values (HOMO or LUMO), where the values increase in the order of dark purple, blue, green, and yellow (Note that all HOMO values are negative). To enhance visualization clarity and mitigate the influence of extreme outliers, we set the color mapping range based on the 3 rd and 97 th percentiles of the property values. It can be observed that the range of values for each HOMO and LUMO property shown in Figure 3 is similar to the range of values represented by the color bar.  
Figure 4 presents the visualization of latent vectors learned by the VAE model for the HOMO property. The molecule located at the upper region in Figure 4, C\({}_{12}\)H\({}_{16}\)N\({}_{4}\), is among the top 3% of molecules in the dataset with the highest HOMO values. This molecule has a relatively high HOMO value of \(-\)3.3442 eV, indicating poor oxidative stability and a potential risk of decomposition at high-voltage cathodes (>4 V). Meanwhile, its LUMO value of \(-\)0.2693 eV suggests good reductive stability, making it a potential electrolyte additive for lithium metal or graphite anodes. However, due to its limited electrochemical window, further structural optimization is required to enhance its stability and performance. Meanwhile, the BF\({}_{3}\) molecule located at the lower part of Figure 4 has a very low HOMO value of \(-\)11.9267 eV, placing it among the bottom 3% of molecules in the dataset with the lowest HOMO values, indicating excellent oxidative stability. Thus, it can function as a stable Lewis acid and a potential electrolyte additive for lithium-ion batteries, particularly for improving SEI layer formation or modifying electrolyte properties. In Figure 4, these two extreme molecules are clustered together with molecules having relatively similar HOMO values. For these electrolyte additives, which are significant but require further structural exploration, our proposed approach can generate new molecules that share similar characteristics with existing electrolyte additive data while introducing various structures. The reconstruction accuracy, calculated by comparing the input SMILES with the reconstructed SMILES, is 97.898%.  
Figure 5 presents the visualization of the latent space of the model trained on the LUMO property and reconstruction accuracy is 98.327%. The first molecule highlighted with a red dashed line at the top in Figure 5 is a bicyclic chiral molecule, potentially exhibiting high thermal stability and unique stereochemical properties. The second molecule is an aromatic compound containing strong electron-withdrawing groups (cyano and fluorine), exhibiting high oxidative stability and strong electrochemical reactivity. Considering that the 256-dimensional vectors were reduced to a lower dimension for visualization, it can be observed that molecules with relatively similar property values are well clustered together. By exploring the latent space based on these valuable electrolyte additive molecules, new molecules can be generated.  
Figure 4: Figure of the latent space visualization for the model trained on each HOMO property. The molecules marked with red dashed lines represent two example molecules from the dataset that fall within the top and bottom 3% extremes of HOMO values.