Electrolytefm improves multiple electrolyte property predictions together
ElectrolyteFM: Unifying Electrolyte Property Prediction through Cross-Property Knowledge Learning
Machine Learning
Summary
Designing electrolytes involves balancing many different properties, but most models only predict a few at a time. The authors found that learning to predict several properties together can help or hurt, depending on the property interactions. They created ElectrolyteFM, a model that learns each property separately but also shares useful information wisely between them. This approach led to more accurate predictions for 12 electrolyte properties and also worked well on new types of electrolytes. The model can better guide the creation of effective electrolytes by understanding multiple properties at once.
What this means in practice
- •For battery developers: Predict multiple electrolyte properties accurately to optimize electrolyte formulations for improved battery performance and safety.
- •For chemical engineers: Use unified property predictions to design electrolytes with balanced characteristics for specialized industrial chemical processes.
Authors
Jiaxin Yu, Shuo Wang, Peng Wang, Yongcai Wang, Deying Li
Abstract
Electrolyte formulation design requires balancing multiple physicochemical properties, yet existing models often focus on a limited subset. Learning each property in isolation can overlook transferable chemical information, whereas indiscriminate sharing can introduce cross-property interference. Our directed transfer analysis shows that jointly learning two property prediction tasks can improve or degrade prediction relative to separate training, with asymmetric transfer effects between the tasks. We propose ElectrolyteFM, a unified multi-property prediction model which can more accurately predict multiple properties of each electrolyte by effectively identifying and utilizing property-specific features and knowledge shared across properties. More specifically, ElectrolyteFM learns property-specific representations independently and captures cross-property knowledge through a separately trained expert pool. A router selects relevant shared information for each formulation and target property, and property-specific residual adapters convert this information into corrections to the corresponding representation for prediction. Experiments on Electrolyte12 show that ElectrolyteFM reduces normalized mean absolute error averaged across 12 electrolyte properties by 14.8% relative to the strongest electrolyte-specific baseline. On an independent sodium-electrolyte dataset unseen during training, it reduces conductivity mean absolute error by 6.7% relative to the best-performing baseline.