June 21, 2026 article Machine learning-assisted materials design, mechanistic insights, and predictive modeling for high performance lithium-sulfur batteries Energy storage materials DOI: 10.1016/j.ensm.2026.105315 OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 1 References 2 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance 2024 Energy and AI article Engineering Advanced Battery Technologies Research Open access 10 citations Unlocking battery insights with interpretable machine learning 2025 Joule article Engineering Advanced Battery Technologies Research 5 citations 2 results