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

2026-02-20

AI Summary

This work addresses the high cost and limited accessibility of operando infrared (IR) spectroscopy for analyzing the solid-electrolyte interphase (SEI) in lithium-ion batteries by introducing a novel task, “Operando IR Prediction,” which aims to infer time-resolved dynamic evolution from a single static spectrum. To this end, the authors present OpIRSpec-7K, the first large-scale operando IR dataset, along with the evaluation benchmark OpIRBench. They further propose ABCC, a physics-aware end-to-end framework that decouples solvent and SEI components via a dual-stream architecture, incorporates a chemically constrained stream to model voltage-driven reaction kinetics, and integrates physical priors such as mass conservation and peak shifts. ABCC significantly outperforms existing static, sequential, and generative models on both seen and unseen battery systems and enables interpretable inference of SEI formation pathways.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Operando IR Prediction
• Solid Electrolyte Interphase
• Infrared Spectroscopy
• Lithium-ion Batteries
• Spectral Dynamics

Innovation

• Operando IR Prediction
• Physics-Informed Modeling
• Chemical Flow
• Solid Electrolyte Interphase
• Spectral Dynamics