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Computer Science > Computation and Language
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High-Throughput Calculations and Machine Learning for Discovering Halide-Type Solid-State Electrolyte Materials
Abstract Click to copy section linkSection link copied! Oxide- and sulfide-based solid-state electrolytes face various intrinsic defect challenges. Developing solid-state electrolyte materials with superior overall performance is critical for the practical implementation of solid-state alkali metal ion batteries. Herein, we utilized high-throughput calculation technique to screen halide-type solid-state electrolytes and resolve the structure–function relationships of halides.
MARS: Memory-Enhanced Agents with Reflective Self-improvement
arXiv:2503.19271 (cs) View PDF HTML (experimental) Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2503.19271 [cs.CL] (or arXiv:2503.19271v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2503.19271 Submission history From: Miao Zhang [ view email] [v1] Tue, 25 Mar 2025 02:05:46 UTC (4,719 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is...
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