# Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model

arXiv: https://arxiv.org/abs/2406.19792 · HTML: https://mlsanity.com/paper/2406.19792/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [SELFormer: Molecular Representation Learning via SELFIES Language Models](https://mlsanity.com/paper/2304.04662/) (2023)
- [Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance](https://mlsanity.com/paper/2307.03811/) (2023)

## Built on by (most cited first, 5 of 5)

- [COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs](https://mlsanity.com/paper/2407.20265/) (2024)
- [LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery](https://mlsanity.com/paper/2503.01022/) (2025)
- [Superior Molecular Representations from Intermediate Encoder Layers](https://mlsanity.com/paper/2506.06443/) (2025)
- [CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction](https://mlsanity.com/paper/2506.12231/) (2025)
- [Geometric Mixture Models for Electrolyte Conductivity Prediction](https://mlsanity.com/paper/2510.15403/) (2025)

## Abstract (arXiv)

Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constituents. Consequently, a strategy that adeptly captures these relationships and forms a robust representation of the formulation is essential for integrating with machine learning models to predict properties accurately. In this paper, we introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods.