  To reduce the experimental validation workload for chemical researchers and
accelerate the design and optimization of high-energy-density lithium metal
batteries, we aim to leverage models to automatically predict Coulombic
Efficiency (CE) based on the composition of liquid electrolytes. There are
mainly two representative paradigms in existing methods: machine learning and
deep learning. However, the former requires intelligent input feature selection
and reliable computational methods, leading to error propagation from feature
estimation to model prediction, while the latter (e.g. MultiModal-MoLFormer)
faces challenges of poor predictive performance and overfitting due to limited
diversity in augmented data. To tackle these issues, we propose a novel method
COEFF (COlumbic EFficiency prediction via Fine-tuned models), which consists of
two stages: pre-training a chemical general model and fine-tuning on downstream
domain data. Firstly, we adopt the publicly available MoLFormer model to obtain
feature vectors for each solvent and salt in the electrolyte. Then, we perform
a weighted average of embeddings for each token across all molecules, with
weights determined by the respective electrolyte component ratios. Finally, we
input the obtained electrolyte features into a Multi-layer Perceptron or
Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world
dataset demonstrate that our method achieves SOTA for predicting CE compared to
all baselines. Data and code used in this work will be made publicly available
after the paper is published.
