## 1 Introduction  
In recent years, advanced deep learning techniques, such as generative models and graph neural networks, have significantly advanced research in various fields, including material discovery for semiconductors, biomedical compounds, and lithium-ion batteries [1, 2, 3, 4, 5, 6]. Especially, graph neural network (GNN) models are well suited for representing molecules with the same structural form, as they utilize a graph-based input format, making them widely used for molecular property prediction [7, 8, 9, 10, 11]. In addition to GNNs, generative models, such as Variational Autoencoders (VAEs), Transformers, and Diffusion models, have been actively explored for molecular design and material discovery [12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. These models enable the generation of novel molecular structures while preserving crucial chemical properties, allowing for an efficient and systematic approach to discovering new materials. VAEs are widely used to capture the latent space representations of molecular structures, facilitating the interpolation and generation of chemically valid compounds [22; 23; 24; 25; 26; 27; 28]. Transformer-based models, which were originally developed for natural language processing tasks, have been successfully adapted to handle sequence-based molecular representations such as SMILES, enabling effective molecular generation [29; 30; 31; 32; 33; 34]. By leveraging these generative models, researchers have significantly advanced the discovery of novel molecules in various fields, including drug development, organic electronics, and battery materials. Recent studies have demonstrated the effectiveness of deep learning in diverse optimization problems, including biomechanics-based gait rehabilitation, reaction-diffusion modeling for micro-disk biosensors, and nonlinear autoregressive neural networks for predicting chemical absorption properties. Inspired by these advancements, our study applies deep generative models and molecular property prediction techniques to optimize electrolyte additive design for lithium-ion batteries. Battery research and development have also explored the application of machine learning and deep learning models [35; 36]. This trend has led to substantial reductions in both the time and cost associated with battery materials discovery. However, previous studies have been limited to predicting battery properties, such as State of Charge (SoC) and battery cycle life, using classical machine learning algorithms or merely proposing new insights [37; 38; 39; 40; 41; 42; 43]. Although large-scale projects such as the Battery 2030+ project are conducting research over a mid-to-long-term period to advance secondary battery technology, their primary focus remains on broader advancements in materials and manufacturing rather than on the targeted development of electrolyte additives [44; 45; 46]. In contrast, this study focuses on the molecular-level design of electrolyte additives specifically for lithium-ion batteries, aiming to accelerate the development process. The electrolyte solution in lithium-ion batteries is typically composed of organic solvents, lithium salts, and carefully selected electrolyte additives, which together determine the overall electrochemical characteristics of the battery system. Electrolyte additives, although present in small quantities, play a vital role in stabilizing the electrochemical environment and optimizing battery efficiency [47; 48]. Specifically, electrolyte additives facilitate lithium-ion transport, expand the operational temperature range, improve electrolyte stability, and promote the formation of a solid electrolyte interphase (SEI) layer during the initial charge-discharge cycle. Among the various functions of electrolyte additives, this study focuses on their role in decomposing prior to the organic solvents during the initial charge of the battery, forming a SEI layer on the electrode surface to provide protection.