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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Advanced hard carbon materials for practical applications of sodium-ion batteries developed by combined experimental, computational, and data analysis approaches
Published in Progress in Materials Science, 2025
This study establishes a literature-data-driven framework for the analysis and design of hard-carbon anodes for sodium-ion batteries. A custom data-collection and processing pipeline was used to curate precursor, synthesis, structural, and electrochemical information from published studies, followed by missing-data imputation and machine-learning modelling. Random Forest and SHAP analyses were employed to identify and interpret relationships between preparation parameters, microstructural characteristics, initial Coulombic efficiency, and reversible capacity. The resulting workflow provides data-informed insights for hard-carbon optimization and a transferable foundation for broader energy-materials research.
Recommended citation: Sun, Z.; Liu, H.; Li, W.; Zhang, N.; Zhu, S.; Chen, B.; He, F.; Zhao, N.; He, C. (2025). "Advanced hard carbon materials for practical applications of sodium-ion batteries developed by combined experimental, computational, and data analysis approaches." Progress in Materials Science 149, 101401.
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How Machine Learning Has Driven the Development of Rechargeable Ion Batteries
Published in Advanced Energy Materials, 2025
This review examines machine-learning workflows for rechargeable battery research, including data acquisition, preprocessing, model development, interpretability, applications across battery chemistries, and emerging closed-loop research paradigms.
Recommended citation: Liu, H.; Li, S.; Zhu, S.; Hu, Y.; Han, X.; Shi, C.; He, F.; He, C.; Chen, B.; Zhao, N. (2025). "How Machine Learning Has Driven the Development of Rechargeable Ion Batteries." Advanced Energy Materials 15, e04095.
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Descriptor-based Machine Learning for Coordination-Engineered Single-Atom Catalysts
Ongoing research, 2026
This work establishes a systematic ML pipeline to extract reliable material design rules from noisy, high-dimensional experimental datasets. By distilling 376,000+ dynamic cycling records into robust performance metrics and implementing symmetry-aware “Rotation Augmentation,” the framework addresses the challenges associated with small-sample laboratory data. The project bridges the gap between raw electrochemical curves and actionable catalyst screening. This data-driven strategy is designed to be transferable to other conversion-type battery systems and to accelerate the discovery of high-retention catalysts.
Data-Driven Discovery of Lithium–Sulfur Catalysts via Two-Stage Large Language Model Fine-Tuning and Mechanistic Validation
Published in Materials Today Energy, 2026
A fully automated, literature-driven framework integrating paper-aware information extraction, domain-specific language-model fine-tuning, multidimensional catalyst evaluation, external consistency assessment, and mechanistic validation for lithium–sulfur battery catalyst screening. Its modular architecture is designed to support adaptation to other materials-discovery tasks through domain-specific data, evaluation criteria, and validation modules.
Recommended citation: Liu, H.; Li, S.; Huang, Y.; Zhao, Z.; Wu, G.; Wang, N.; Sun, Z.; Liu, C.; Yan, H.; Wu, Z.; Wang, Z.; Liu, E.; Shan, Z.; Shi, C.; He, F.; He, C.; Zhao, N.; Tang, Y.; Hu, Y.; Chen, B. (2026). "Data-Driven Discovery of Lithium–Sulfur Catalysts via Two-Stage Large Language Model Fine-Tuning and Mechanistic Validation." Materials Today Energy 59, 102371.
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talks
AI-Driven Paradigms for Accelerating Battery Materials Discovery
Published:
In this talk, I presented our progress in applying machine learning and large language models to battery-material design and literature-driven catalyst screening.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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