From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark

TL;DR

This paper introduces a physics-informed deep learning framework for predicting the dynamic evolution of infrared spectra in lithium-ion batteries, leveraging a large dataset and benchmark to enhance understanding of SEI formation.

Contribution

It presents the first large-scale operando IR dataset, a new benchmark, and a novel physics-aware model that improves spectral prediction and interpretability in battery analysis.

Findings

ABCC outperforms existing models in spectral prediction accuracy.

The framework generalizes well to unseen battery systems.

Enables interpretable insights into SEI formation pathways.

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 Chemical Flow to explicitly model reaction trajectories, employs a two-stream disentanglement mechanism for solvent-SEI separation, and enforces physics and spectrum constraints such as mass conservation and peak shifts. ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines. ABCC even generalizes to unseen systems and enables interpretable downstream recovery of SEI formation pathways, supporting AI-driven electrochemical discovery.