eScholarship
metadata only
Modeling Chemical Reactivity for Battery Applications through Data-driven Methodologies
The following dissertation discusses the usage of data-driven approaches, combined with density functional theory (DFT), to study the complex reactions involved in the battery systems, e.g., electrolyte degradation and solid electrolyte interphase (SEI) formation. Data-driven approaches include the
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation#987747
- Beyond Isolated Optimization: A Holistic Review Across the Pre-Mid Post-Treatment Chain for Hard Carbon in Sodium-Ion Battery.#266877
- Intramolecular Design of Poly(ethylene oxide) for Solid-State Electrolytes and Next-Generation High-Energy Batteries.#209507
- Gas-mediated defect engineering in earth-abundant Mn-rich layered oxides for non-aqueous sodium-based batteries#495517
- BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking#363361
Record · ID 1106831
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive