A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy

Authors: Naoki Wada, Yuta Kimura, Masaichiro Mizumaki, Koji Amezawa, Ichiro Akai, Toru Aonishi

Published: February 23, 2026

Quantitative understanding of coupled reaction and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be measured directly. In this study, we develop a physics-integrated, data-driven analysis pipeline to estimate internal electrochemical states from operando microscopic X-ray absorption fine structure ($μ$-XAFS) hyperspectral data of LIB cathodes with LiPF$_6$ electrolyte. State-of-charge (SOC) maps are first constructed from Co K-edge spectra. To resolve ambiguities in the two-phase reaction region, a physics-regularized three-layer neural network is introduced, enforcing spatial continuity of SOC and current conservation. The inferred SOC dynamics are then incorporated into a Kirchhoff-based Markov random field framework that integrates Kirchhoff's current and voltage laws, Ohm's law, and a symmetric Butler-Volmer relation to estimate interfacial current, ionic current, electrolyte potential, and effective ionic conductivity. Application to composite electrodes with different initial electrolyte concentrations (0.3, 1, and 2M LiPF$_6$) reveals distinct reaction propagation behaviors governed by electrolyte concentration-dependent conductivity. The inferred electrolyte concentration distributions show qualitative agreement with independent operando X-ray transmission imaging performed on LIB composite cathodes employing a LiAsF$_6$ electrolyte. This framework enables quantitative visualization of otherwise inaccessible internal transport phenomena.

Transport in close-packed solids with stacking defects

Authors: C. M. Wilson, R. Ganesh, K. V. Samokhin

Published: February 20, 2026

Lithium and sodium are the only solids that are known to lose crystalline order upon cooling. The seemingly-disordered low-temperature phase shows signatures of various close-packed structures. The lack of order has been attributed to a hidden gauge symmetry that arises when electrons from one layer can hop to a neighbouring layer but not further. It makes all close-packed structures nearly degenerate and leads to ``structural frustration''. In this article, we examine whether this symmetry is reflected in transport signatures. Taking advantage of in-plane translational periodicity, we map the bulk Bloch Hamiltonian to an effective one-dimensional chain, with stacking disorder mapping to random phases of the hopping amplitudes. We derive an explicit analytic form for the Green's function of electrons and use it to calculate conductance of a bulk crystal. When hopping in the effective one-dimensional chain is restricted to nearest neighbours, conductance is completely insensitive to phase disorder, which indicates that all close-packed structures exhibit the same conductance. We show that the leading correction that can differentiate between close-packed structures arises from hopping to the next-nearest-neighbour layer, equivalent to second-neighbour hopping in the chain model. This process appears when a pair of next-neighbour layers are aligned in a certain way, e.g., at an hcp-like stacking fault within an fcc background. With this hopping included, conductance becomes sensitive to the precise arrangement of layers. When multiple stacking faults are present, the conductance decreases with increasing system size, as expected from Anderson localization. Our results are applicable to pressurized lithium and sodium, where conductance measurements can identify and characterize stacking faults.

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

Published: February 20, 2026

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.

PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes

Authors: Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang, Pao-Hsiung Chiu, My Ha Dao, Nagarajan Raghavan, Chinchun Ooi

Published: February 20, 2026

Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending operational lifespan. Here, we introduce PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes), a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm to enable rapid, scalable, and interpretable parameter inference with potential for application to next-generation batteries. The meta-learned PINN utilizes fundamental physics principles to achieve accurate zero-shot prediction of electrode behavior with test errors below 0.1$\%$ while maintaining an order-of-magnitude speed-up over conventional solvers. PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, recovering the evolution of key internal state parameters such as Li-ion diffusion coefficients across usage cycles. Notably, the inferred cycle-dependent evolution of these parameters exhibit consistent trends across different batteries without any customized degradation physics-embedded heuristic, highlighting the effective regularizing effect and robustness that can be conferred through incorporation of fundamental physics in PINEAPPLE. By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-cell and intra-cell variability of battery modules and battery packs, thereby unlocking new opportunities for downstream on-the-fly needs in next-generation battery management systems such as individual cell-scale state-of-health diagnostics.

Lithium as a probe of stellar and galactic physics

Authors: C. Charbonnel, N. Prantzos

Published: February 19, 2026

Lithium plays a unique role in astrophysics, as it is a powerful diagnostic for the physics and evolution of low-mass stars, Galactic archaeology, and cosmology. We review the Li observations in stars at different phases of their evolution, the strengths and the limitations of the current theoretical stellar models to explain the Li abundance data, our understanding of the Li sources and of the evolution of Li through- out the Galactic history. Key takeaways from the current state of the research in the field are: 1) Stellar evolution models accounting for fundamental transport processes of chemical species and angular momentum hold the promise of providing a common stellar Li depletion explanation to the Li abundance patterns observed in all Galactic stellar populations, including the dip and the plateau(s). 2) Novae are most probably the main source of Li in the Galaxy, on observational (but not yet theoretically established) grounds. 3) Radial migration of stars in the Galactic disk holds the key to understand many aspects of the Li evolution in the Milky Way.

Multi-Method Li Plating Characterization of a Commercial 26 Ah Li-Ion Pouch-Cell

Authors: Christiane Rahe, Heinrich Ditler, Thorsten Tegetmeyer-Kleine, Marius Flügel, Thomas Waldmann, Margret Wohlfahrt Mehrens, Philipp Schleker, Peter Jakes, Beatrice Wolff, Josef Granwehr, Rüdiger-A. Eichel, Jiří Vacík, Giovanni Ceccio, Antonino Cannavo, Ivana Pivarníková, Ralph Gilles, Peter Müller-Buschbaum, Adrian Mikitisin, Joachim Mayer, Michael Noyong, Ulrich Simon, Marius Bolsinger, Volker Knoblauch, Dirk Uwe Sauer

Published: February 19, 2026

Lithium (Li) plating on graphite is a significant degradation mechanism in Li-ion batteries. While numerous experimental techniques have been used to study Li plating in laboratory cells, investigations of commercial high-energy cells often rely on electrochemical methods. Here we present and classify various methods for detecting Li plating on a commercial A123 pouch cell. In a round robin study across multiple battery research laboratories, Li-plated graphitic electrode material was analyzed using electrochemical, microscopic, and spectroscopic methods capable of detecting metallic Li deposits. After cell opening, their overall distribution on the anode surface was examined using a flatbed scanner to ensure comparability of the samples. Optical and electron microscopy provided detailed surface and, in combination with a focused ion beam, subsurface structure and morphology. Spectroscopic methods confirmed the presence and onset of plated Li with varying sensitivity. Moreover, spectroscopic and imaging techniques were combined correlatively where possible. Availability and measurement duration of each technique was compared. Optical methods are fast and easy to use; thus, they are recommended for most samples, with spectroscopic confirmation reserved for reference samples. This multimodal study demonstrates a range of methods that can be used alone or in combination to qualitatively or quantitatively detect Li-plating.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds

Authors: G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, A. Melnikov

Published: February 18, 2026

Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason we apply multi-objective hyperparameter optimization to the two objectives. Additionally, we experiment with modified architectures by making variants of Allegro some by adding strictly classical multi-layer perceptron (MLP) layers and some by adding quantum-classical hybrid layers. We compare the results from QM9, rMD17-aspirin, rMD17-benzene and our own proprietary dataset consisting of copper and lithium atoms. As results, we have a list of variants that surpass the Allegro in accuracy and also results which demonstrate the trade-off with inference times.

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-ion Battery Chemistry by Mass Spectrometry and Electro-Chemical Reaction Networks

Authors: Mona Abdelgaid, Oliver Hvidsten, Theo Sombret, Egon Kherchiche, Julien Maillard, Antonin Gajan, Patrick Bernard, Kamila Kazmierczak, Mauricio Araya-Polo, Germain Salvato Vallverdu, Carlos Afonso, Pierre Giusti, Kristin A. Persson

Published: February 17, 2026

The formation and stability of the solid electrolyte interphase (SEI) play a central role in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI's heterogeneous, dynamic, and multi-phase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational-experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electro-chemical reaction networks (eCRNs), stochastic algorithms, and Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonate-based electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in literature and predicted 28 novel SEI species, nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectra analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational-experimental approach deepens our molecular-level understanding of SEI chemistry and supports the rational design of advanced electrolytes and engineered interphases for next-generation lithium-based batteries.

Description of nucleon elastic scattering off $^6$Li with the four-body continuum-discretized coupled-channels method

Authors: Kazuyuki Ogata, Shoya Ogawa

Published: February 17, 2026

Background: Neutron reactions off lithium isotopes up to 50 MeV are important for nuclear data science, around the International Fusion Material Irradiation Facility (IFMIF) facility in particular.
  Purpose: We aim at constructing a semi-microscopic reaction model that describes neutron elastic scattering off $^6$Li up to 50 MeV taking the breakup channels of $^6$Li into account.
  Methods: We adopt the continuum-discretized coupled-channels method (CDCC) with an $α+p+n$ three-body model of $^6$Li. We employ the $g$-matrix effective interaction by Jeukenne, Lejeune, and Mahaux (JLM). The renormalization factors of the real and imaginary parts of the JLM interaction are treated as free parameters.
  Results: The renormalization parameter of the real part of the JLM interaction is found to be constant ($=1.1$), whereas that for the imaginary part has a smooth energy dependence. The four-body CDCC calculation with these parameters well describes the angular distributions of both proton and neutron elastic scatterings as well as the neutron total cross section and proton total reaction cross section. The applicable energy range is found to be from 7 MeV to 50 MeV.
  Conclusions: We have constructed a reliable reaction model for describing nucleon-$^6$Li scattering between 7 MeV and 50 MeV. This model can directly be applied to inelastic scattering and break reactions for $^6$Li with the help of the complex scaling method.

Isotope effect in the work function of lithium

Authors: Atef A. Sheekhoon, Abdelrahman O. Haridy, Vitaly V. Kresin

Published: February 17, 2026

The work functions of 7Li and 6Li metals have been measured as a function of temperature, by using photoionization of pure isolated metal nanoparticles in a beam. These data reveal a marked isotope effect in the temperature variation of these work functions. Furthermore, for both isotopes the curvature of this temperature variation is found to be significantly larger than may be ascribed purely to a change in the electron gas density. These findings enhance the characterization of lithium as a quantum material in which the interplay between electronic and ionic degrees of freedom is nontrivial, and call for a microscopic understanding beyond simple models. Additionally, the slope of the work function curves was observed to vanish in the low temperature limit, as had been predicted on the basis of the Third Law of thermodynamics.