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[2602.18551] From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark
Summary
This paper presents a novel approach to predicting operando infrared dynamics in lithium-ion batteries using a physics-informed flow modeling framework, addressing the challenges of analyzing the Solid Electrolyte Interphase (SEI).
Why It Matters
The analysis of SEI is crucial for enhancing lithium-ion battery performance. This research introduces a large-scale dataset and a new modeling framework that improves the understanding of chemical dynamics, potentially accelerating advancements in battery technology and AI-driven electrochemical discovery.
Key Takeaways
- Introduces OpIRSpec-7K, a dataset with 7,118 samples for operando IR prediction.
- Presents the Aligned Bi-stream Chemical Constraint (ABCC) framework for modeling SEI dynamics.
- Demonstrates significant performance improvements over existing models in predicting chemical reactions.
- Enables interpretable recovery of SEI formation pathways, supporting AI applications in electrochemistry.
- Addresses the limitations of traditional spectroscopy methods in battery research.
Physics > Chemical Physics arXiv:2602.18551 (physics) [Submitted on 20 Feb 2026] Title:From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark Authors:Shuquan Ye, Ben Fei, Hongbin Xu, Jiaying Lin, Wanli Ouyang View a PDF of the paper titled From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark, by Shuquan Ye and Ben Fei and Hongbin Xu and Jiaying Lin and Wanli Ouyang View PDF HTML (experimental) Abstract:The Solid Electrolyte Interphase (SEI) is critical to the performance of lithium-ion batteries, yet its analysis via Operando Infrared (IR) spectroscopy remains experimentally complex and expensive, which limits its accessibility for standard research facilities. To overcome this bottleneck, we formulate a novel task, Operando IR Prediction, which aims to forecast the time-resolved evolution of spectral ``fingerprints'' from a single static spectrum. To facilitate this, we introduce OpIRSpec-7K, the first large-scale operando dataset comprising 7,118 high-quality samples across 10 distinct battery systems, alongside OpIRBench, a comprehensive evaluation benchmark with carefully designed protocols. Addressing the limitations of standard spectrum, video, and sequence models in capturing voltage-driven chemical dynamics and complex composition, we propose Aligned Bi-stream Chemical Constraint (ABCC), an end-to-end physics-aware framework. It reformulates MeanFlow and introduces a novel Chemi...
