## 3 Results
### Results and Experimental Setup
#### 3.2.2 Generation  
Generation was performed through random sampling within a radius of 1 around arbitrarily selected molecules in the latent space. To verify the chemical and physical validity of the generated molecules before the experimental stage and DFT calculations, the following metrics were selected. Finally, we evaluated the generated molecules using validation, novelty, fragment similarity (Frag), scaffold similarity (Scaff), internal diversity (IntDiv), PB Valid, and SA score metrics [61, 62]. Table 2 presents the evaluation results of the generated molecules based on below metrics.  
PB Valid is a measure of the probability that a generated molecule is chemically valid, considering polymer-based validity checks. A high PB Valid score indicates that most generated molecules adhere to fundamental chemical constraints.  
Validity represents the proportion of generated molecules that are chemically valid according to standard valency rules. A score of 1.0 implies that all generated molecules conform to these constraints.  
Novelty quantifies the proportion of generated molecules that are not present in the training dataset. A high novelty score, such as 1.0, indicates that all generated molecules are unique and not direct replicas of known structures.  
Fragment similarity measures the similarity of generated molecular fragments to those found in the training dataset. This metric helps assess whether the generated molecules maintain meaningful substructures from known compounds.  
Scaffold similarity evaluates how structurally similar the core scaffolds of generated molecules are to those in the training dataset. Lower scaffold similarity values suggest that the model generates more structurally diverse molecules.  
Internal diversity (IntDiv) quantifies the diversity within the set of generated molecules. A high internal diversity score indicates that the generated molecules cover a broad range of structural variations rather than being highly redundant.  
Synthetic accessibility score (SA score) is a heuristic estimate of how easily a molecule can be synthesized. It ranges from 1 (easily synthesizable) to 10 (very difficult to synthesize). Lower SA scores suggest that the generated molecules are more practical for real-world synthesis.  
Since the MOSES framework can measure similarity by comparing with the training dataset, it can be observed from Table 2 that the generated molecules have low similarity to the training dataset while being easy to synthesize and composed of valid molecules. Figure 6 represents the distribution of properties, including HOMO, LUMO, and molecular weight, for approximately 1000 newly generated molecules. If the molecular weight of an  
Figure 5: Figure of the latent space visualization for the model trained on each LUMO property. As the LUMO values increase, they appear more yellow, while lower values are represented by a deep purple color.  
electrolyte additive is too high, it becomes difficult to dissolve in organic solvents. Therefore, the molecular weight should not deviate significantly from the average (183; see Table 1) and the distribution of the electrolyte additive candidate dataset. Figure 6 shows that the molecular weight distribution of the generated molecules has a high frequency in the range of 50-200, while also exhibiting a diverse range of HOMO and LUMO values. This suggests that highly unrealistic molecules have been filtered out, and new molecules with characteristics suitable for electrolyte additives have been successfully generated. Examples of the generated molecules can be found in Appendix B.