Reusability report: Exploring the utility and extensibility of an integrated modelling framework for liquid electrolyte design - Nature

Abstract

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.

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Data availability

The datasets used in this study are available via HuggingFace at https://huggingface.co/datasets/PKUAIBDA/Dataset_Bamboomixer_extension. All of the data used in the original paper for testing is available via HuggingFace at https://huggingface.co/ByteDance-Seed/bamboo_mixer.

Code availability

The original source code is available via Zenodo at https://doi.org/10.5281/zenodo.17694573 (ref. 24). Our extended framework used in this work is also available via Zenodo at https://doi.org/10.5281/zenodo.19048302 (ref. 25). The recommended default configurations are available via Zenodo at https://doi.org/10.5281/zenodo.19876420 (ref. 26).

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Acknowledgements

We thank Z. Yang, S. Gong and W. Yan for their support with the code and data.

Funding

This work was supported by the Advanced Materials-National Science and Technology Major Project (2025ZD0618801), the National Natural Science Foundation of China (12426301), and AI for Science (AI4S)-Preferred Program, Peking University, Shenzhen, China.

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Conceptualization: G.L., J. Zheng and C.O. Methodology: G.L. and J. Zhao. Investigation: G.L., J. Zhao, Z.L., R.Z., H.L., Q.Z., F.R., C.F., Q.L., Y.Z., B.X., J. Zheng and C.O. Supervision: J. Zheng and C.O. Writing: G.L., J. Zhao, Y.Z., J. Zheng and C.O.

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Nature Machine Intelligence thanks Ying Wang and Kai Yang for their contribution to the peer review of this work.

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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

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- DOI: https://doi.org/10.1038/s42256-026-01277-x