Despite rapid advances in machine learning-driven electrolyte design, the robustness, transferability and applicability of existing models remain insufficiently explored. Yang et al. recently introduced a unified machine learning framework for electrolyte formulation design. The framework adopts a physics-informed architecture that explicitly integrates molecular structural representations with formulation-level compositional information while preserving permutation invariance. By coupling forward property prediction with inverse generation, Yang et al. achieved accurate property prediction and efficient exploration of the formulation space. Here we present a systematic evaluation and multiscale extension of the framework proposed by Yang et al. We assess its robustness and reproducibility through rigorous benchmarking. We also reveal how training data size and compositional heterogeneity govern its applicability by quantifying its sensitivity to data distribution. Furthermore, we demonstrate cross-system transferability through zero-shot and few-shot learning across diverse operational regimes and novel electrolyte compositions. A multiscale extension of the framework has been implemented to encompass diverse targets, ranging from fundamental physical properties to electronic energy boundaries and battery Coulombic efficiency, where it substantially outperforms standard baselines. Overall, this work reveals both the potential and limitations of the framework proposed by Yang et al. across different systems and properties, and it establishes a reference for the reliable application and broader adoption of artificial intelligence-driven electrolyte design.
Lai, G., Zhao, J., Liu, Z. et al. Reusability report: Exploring the utility and extensibility of an integrated modelling framework for liquid electrolyte design.
Nat Mach Intell (2026). https://doi.org/10.1038/s42256-026-01277-x
https://doi.org/10.1038/s42256-026-01277-x
