## 2 Materials and Methods
### Model's Architecture  
In the field of 3D molecule generation, numerous architectures have been employed to generate new molecules. Among these, the NPVAE model was selected to generate optimal electrolyte additive molecules for lithium-ion batteries and predict their electrochemical properties for the following four reasons [54]:  
* Architecture aligned with our requirements and objectives. The dataset described in the following Section 2.1 includes significant candidate molecules used in lithium-ion battery electrolytes. We aim to extract meaningful and valuable information from this high-quality dataset to explore new molecules that could serve as potential electrolyte additives. Additionally, we require accurate predictions of electrochemical parameters such as HOMO/LUMO, which are critical for electrolyte additives. If new materials can be discovered and their HOMO/LUMO values predicted without direct experimentation, lithium-ion battery researchers would be able to anticipate experimental results quickly and efficiently. The VAE-based model with its continuous latent space representation fits these requirements and objectives, enabling us to explore the desired chemical space effectively. * Performance. Among the VAE-based 3D molecule generation models NPVAE demonstrates the best performance outperforming models such as ChemicalVAE, CG-VAE, JT-VAE, and HierVAE, with a 2D reconstruction accuracy of 0.813 [22; 26; 27; 28]. Based on experimental results from the literature NPVAE was deemed the most suitable model for achieving our performance goals. * Utilization of structural information. Deep learning models that handle large sets of molecules typically employ various molecular representation methods. While SMILES is a simple and widely used format, it has limitations in capturing full structural information [29]. The NPVAE model overcomes this by converting SMILES inputs into graph-based representations, incorporating structural information through the use of a Tree-LSTM model. * Chirality handling. Chirality refers to the geometric property of a molecule where its mirror image cannot be superimposed on the original structure, which is crucial in many chemical applications including pharmaceuticals. The NPVAE model by utilizing 3D molecular structures can effectively incorporate chirality enabling more accurate modeling of stereochemical properties. In NPVAE, the model determines whether a molecule is chiral or not by incorporating chirality information from its ECFP. The architecture of NPVAE comprises a Tree-LSTM-based encoder, an MLP-based decoder, and an additional MLP designed for molecular property prediction [57]. It is a graph-based VAE that integrates fragment-based decomposition, tree-structured representations, and a Tree-LSTM to effectively capture the structural information of compounds, showcasing strong performance in molecular design tasks. We trained the NPVAE model using the electrolyte additive dataset from Section 2.1, based on the HOMO and LUMO properties of each molecule. Figure 2 illustrates the flowchart representing the model's training process and generation process. During the training process, the model followed the black arrows, reconstructing the input molecules while simultaneously predicting their HOMO and LUMO properties. After training, during the generation process, the model followed the red arrows, exploring the latent space to obtain the latent vector \(z\). Using the decoder and regressor, newly sampled molecules and their predicted property values were generated. The detailed preprocessing, encoding, and decoding processes can be found in Appendix A. By training the model to reconstruct the input data while simultaneously training a molecular property prediction model that takes the latent variable \(z\) as input, molecular properties are integrated intothe latent space. This approach enables the latent space to jointly represent both structural and functional information of the molecules.