arXiv subjects

Martin Z. Bazant

Publications and source records attributed to Martin Z. Bazant.

At least 19 recordsLinked to original sources

arXiv2026

Effective reorganization energy for electron transfer

The Marcus theory expression for the rate of non-adiabatic electron transfer is widely used across a range of physical conditions. Although Marcus theory defines the reorganization energy classically, here we show that the reorganization parameter appearing in the activation barrier for normal-region electron transfer is most generally a quantum mechanical object that depends on the electronic coupling, coinciding with the Marcus picture only in the limit of vanishing electronic coupling. This result unifies the physical description of electron-transfer activation barriers across the adiabatic and non-adiabatic regimes and formally predicts that Marcus-like rate expressions remain accurate beyond their traditional non-adiabatic domain of validity. These insights allow us to derive a closed-form expression for the curvature of the current-overpotential relation for electron-transfer-limited reactions at the electrochemical interface, now formally applicable to both inner-sphere and outer-sphere processes.

quant-ph↗

arXiv2026

Overlimiting Ion Transport and Reaction Limitations in Charged Porous Media

Electrochemical reaction rates are controlled by both interfacial charge-transfer kinetics and reactive-ion transport. In charged porous media, fixed charges enrich reactive counterions near pore walls, enabling transport beyond the classical diffusion limit via surface conduction (SC). Under overlimiting conditions, classical Butler-Volmer kinetics predict indefinitely increasing current with overpotential, contrasting with microscopic electron-transfer theories, which impose a finite reaction-limited current. Here, we couple the one-dimensional leaky membrane model to coupled ion-electron transfer (CIET) kinetics to examine the interplay between transport and reaction limitations. The limiting behavior is governed by the scaled surface charge $(\tildeρ_s)$ and a Damköhler number $(Da)$ comparing reaction-limited and diffusion-limited currents. We derive analytical limiting-current expressions for neutral, positive, and negatively charged porous media, mapping underlimiting-to-overlimiting transitions in the $(Da,\tildeρ_s)$ plane. By preventing reactive-ion depletion, SC restores polarization-curve sensitivity to charge-transfer kinetics that would otherwise be obscured by diffusion limitation. Fitting to published Cu electrodeposition data in charged AAO membranes yields $Da\approx24$. CIET parameters fitted to AAO($-$) also describe AAO($+$) and predict a finite AAO($-$) reaction limit beyond the applied voltage range. These results provide a framework for distinguishing transport-limited and reaction-limited responses in electrochemical systems and establish charged porous media as platforms to reveal electrochemical reaction-kinetic descriptors.

physics.chem-ph↗

arXiv2026

Scaling and Analytical Approximation of Porous Electrode Theory for Reaction-limited Batteries

Porous electrode theory (PET) provides essential insights into electrochemical states, but its computational complexity hinders real-time control and obscures scaling relations. To bridge the gap between high-fidelity simulations and reduced-order models, we present a framework of scaling analysis and analytical approximations. By assuming high-performance electrodes minimize transport limitations and overpotentials, we derive a simplified "lean model" governed by four dimensionless numbers: (i) a traditional Damköhler number, $Da$, scaling the characteristic reaction rate to the diffusion rate in the electrolyte-filled pores; (ii) the "process Damköhler number," $Da_p$, scaling the reaction rate to the applied capacity utilization rate (C-rate); (iii) the "wiring Damköhler number," $Da_w$, scaling the reaction rate to an effective electromigration rate for ions in the pores in series with electrons in the conducting matrix; and (iv) the "capacitive Damköhler number," $Da_c$, comparing the rates of Faradaic reactions and double-layer charging. For batteries, we derive analytical solutions for standard protocols, including galvanostatic discharge, chronoamperometry, and electrochemical impedance spectroscopy. Validated against numerical simulations of a practical NMC half-cell, our formulae show excellent agreement at negligible computational cost. This interpretable, physics-based framework accelerates battery design and state estimation while unifying the modeling of batteries, supercapacitors, fuel cells, and other porous electrode systems.

eess.SY↗

arXiv2026

Ab Initio Free Energy Surfaces for Coupled Ion-Electron Transfer

Although coupled ion-electron transfer (CIET) has emerged as a powerful framework to rationalize the kinetics of Faradaic reactions, its mechanism has lacked a fully microscopic, first-principles description. Here we show that the coupling of electron-transfer to ion-transfer can be understood as an extension of Marcus theory in which the ensemble of diabatic nuclear configurations is conditioned on a classical collective variable describing the interfacial anisotropy. This formalism enables direct construction of the CIET free-energy surface from constrained ab initio trajectories, providing a first-principles route to electrochemical current-overpotential relations. We demonstrate this method for CO2 redox on a gold electrode and find that the resulting two-dimensional saddle-point barriers differ substantially from one-dimensional treatments that consider only electron- or ion-transfer coordinates individually.

physics.chem-ph↗

arXiv2026

Thermodynamic effects of solid electrolyte interphase formation from solvation and ionic association in water-in-salt electrolytes

Water-in-Salt-Electrolytes (WiSEs) are a promising class of next-generation electrolytes. Unlike classical dilute electrolytes or more conventional battery electrolytes, WiSEs are characterised by their super-concentrated salt concentration with only a small amount of water, which gives rise to their expanded electrochemical stability window (ESW). The expansion of the ESW is, in part, due to the formation of an inorganic solid electrolyte interphase (SEI) that passivates the anode; this principle is also important in graphite and Li-metal anodes, and beyond Li-ion technologies. The solvation and ionic associations are key descriptors in understanding the expansion of the ESW. Specifically, as reactions which lead to the SEI (or cathode electrolyte interphase, CEI) must occur at the electrode-electrolyte interface, the distribution of reactants and their various solvation environments are critical. This distribution near the interface is referred to as the electrical double layer (EDL), in the absence of reactions. Here we further develop and analyse a recently proposed thermodynamic theory of hydration and ionic associations in the EDL of WiSEs. We parameterize this theory from bulk molecular dynamics simulations and benchmark it against EDL simulations, finding good qualitative agreement. Using this thermodynamic theory, we rationalise changes in the ESW through: changes in the activity in the bulk electrolyte through the Nernst equation, which directly changes the stability of the electrolytes; and thermodynamic changes to the kinetics of these reactions, from the Butler-Volmer equation and coupled ion electron transfer kinetics, through the concentration of reactant species in the Helmholtz layer.

cond-mat.soft↗

arXiv2025

Crossover Dynamics of Non-Fickian Ionic Diffusion in Solids

Ionic diffusion in solids is central to energy storage, electronics, and catalysis, yet its chemical origins are difficult to resolve because conventional diffusion models struggle with effects of confinement, crystallographic disorder, lattice distortions, and coupling to electronic or phononic carriers. These challenges are especially pronounced in battery materials, where ionic and electronic motion occur together, complicating interpretation of electrochemical measurements. Here we use tracer exchange as a direct, non-electrochemical probe to reveal distinct ion-transport regimes in the one-dimensional conductor olivine Li_xFePO4 (0 <= x <= 1). Lithium isotope exchange validates single-file diffusion governed by strong ion-ion correlations, where 1D confinement suppresses bypassing and preserves spatial order. Kinetic Monte Carlo simulations and chronoamperometry quantify both Faradaic and non-Faradaic surface exchange, identifying electron transport, rather than Li+ mobility, as the rate-limiting step for electrochemical reaction. In addition, Li-Na exchange exhibits apparent superdiffusion, with rates that increase with Na content. Simulations attribute this behavior to surface-exchange limitations and Na-induced lattice strain that enhances cross-channel Li+ hopping and drives a crossover from 1D to quasi-2D transport. Four-dimensional STEM, in situ synchrotron XRD, X-ray absorption spectroscopy, and Mossbauer spectroscopy confirm that lattice softening and concerted polaron motion contribute to the observed dynamics. These results establish tracer exchange as a powerful tool for probing coupled ion-electron transport and provide chemical insight into how lattice mechanics and multicomponent exchange shape ionic diffusion in solids.

cond-mat.mtrl-sci↗

arXiv2025

Deep learning denoising unlocks quantitative insights in operando materials microscopy

Operando microscopy provides direct insight into the dynamic chemical and physical processes that govern functional materials, yet measurement noise limits the effective resolution and undermines quantitative analysis. Here, we present a general framework for integrating unsupervised deep learning-based denoising into quantitative microscopy workflows across modalities and length scales. Using simulated data, we demonstrate that deep denoising preserves physical fidelity, introduces minimal bias, and reduces uncertainty in model learning with partial differential equation (PDE)-constrained optimization. Applied to experiments, denoising reveals nanoscale chemical and structural heterogeneity in scanning transmission X-ray microscopy (STXM) of lithium iron phosphate (LFP), enables automated particle segmentation and phase classification in optical microscopy of graphite electrodes, and reduces noise-induced variability by nearly 80% in neutron radiography to resolve heterogeneous lithium transport. Collectively, these results establish deep denoising as a powerful, modality-agnostic enhancement that advances quantitative operando imaging and extends the reach of previously noise-limited techniques.

cs.CV↗

arXiv2025

Thermodynamically Consistent Continuum Theory of Magnetic Particles in High-Gradient Fields

Magnetic particles underpin a broad range of technologies, from water purification and mineral processing to bioseparations and targeted drug delivery. The dynamics of magnetic particles in high-gradient magnetic fields-encompassing both their transport and eventual capture-arise from the coupled interplay of field-driven drift, fluid advection, and particle-field feedback. These processes remain poorly captured by existing models relying on empirical closures or discrete particle tracking. Here, we present a thermodynamically consistent continuum theory for collective magnetic particle transport and capture in high-gradient fields. The framework derives from a free-energy functional that couples magnetic energy, entropic mixing, and steric interactions, yielding a concentration-dependent susceptibility via homogenization theory. The resulting equations unify magnetism, mass transport, and momentum balances without ad hoc shut-off criteria, allowing field shielding, anisotropic deposition, and boundary-layer confinement to emerge naturally. Simulations predict canonical capture morphologies-axially aligned plumes, crescent-shaped deposits, and nonlinear shielding-across field strengths and flow regimes, consistent with trends reported in prior experimental and modeling studies. By organizing captured particle mass data into a dimensionless phase diagram based on the Mason number, we reveal three distinct regimes-thermodynamically controlled, transitional, and dynamically controlled. This perspective provides a predictive platform for in silico optimization and extension to three-dimensional geometries, and informing digital twin development for industrial-scale high-gradient magnetic separation processes.

cond-mat.stat-mech↗

arXiv2025

Learning Reaction-Diffusion Kinetics from Mechanical Information

A central challenge in materials science is characterizing chemical processes that are elusive to direct measurement, particularly in functional materials operating under realistic conditions. Here, we demonstrate that mechanical strain fields contain sufficient information to reconstruct hidden chemical kinetics in coupled chemomechanical systems. Our partial differential equation-constrained learning framework decodes concentration-dependent diffusion kinetics, thermodynamic driving forces, and spatially heterogeneous reaction rates solely from mechanical observations. Using battery electrode materials as a model system, we demonstrate that the framework can accurately identify complex constitutive laws governing three distinct scenarios: classical Fickian diffusion, spinodal decomposition with pattern formation, and heterogeneous electrochemical reactions with spatial rate variations. The approach demonstrates robustness while maintaining accuracy with limited spatial data and reasonable experimental noise levels. Most significantly, the framework simultaneously infers multiple fundamental processes and properties, including diffusivity, reaction kinetics, chemical potential, and spatial heterogeneity maps, all from mechanical information alone. This method establishes a paradigm for materials characterization, enabling accurate learning of chemical processes in energy storage systems, catalysts, and phase-change materials where conventional diagnostics prove difficult. By revealing that mechanical deformation patterns serve as information-rich fingerprints of the underlying chemical processes, this work follows the pathway of inversely learning constitutive laws, with broad implications in materials science and engineering.

cond-mat.mtrl-sci↗

arXiv2025

Theory of ultrafast conductance modulation in electrochemical protonic synapses by multiphase polarization

Three-terminal electrochemical ionic synapses (EIoS) have recently attracted interest for in-memory computing applications. These devices utilize electrochemical ion intercalation to modulate the ion concentration in the channel material. The electrical conductance, which is concentration dependent, can be read separately and mapped to a non-volatile memory state. To compete with other random access memory technologies, linear and symmetric conductance modulation is often sought after, properties typically thought to be limited by the slow ion diffusion timescale. A recent study by Onen et al.[1] examining protonic EIoS with a tungsten oxide (WO3) channel revealed that this limiting timescale seemed irrelevant, and linear conductance modulation was achieved over nanosecond timescales, much faster than the bulk ion diffusion. This contrasts with previous studies that have shown similar conductance modulation with pulse timescales of milliseconds to seconds. Understanding the phenomena behind these conductance modulation properties in EIoS systems remains a crucial question gating technological improvements to these devices. Here, we provide a theoretical explanation that demonstrates how linearity and symmetry arise from consistent control over the electrolyte-WO3 interface. Comparing these past works, changes in the WO3 channel crystallinity were identified, affecting material thermodynamics and revealing that the device achieving nanosecond pulse timescales underwent phase separation. Coupling of electric field polarizatino and increased electron conductivity in high-concentration filaments, the reaction environment at the gate electrode can be controlled, resulting in ideal conductance modulation within the diffusion-limited regime. This work highlights the potential for phase-separating systems to overcome the traditional diffusion barriers that limit EIoS performance.

cond-mat.mes-hall↗

arXiv2025

Mechanics Informatics: A paradigm for efficiently learning constitutive models

Efficient and accurate learning of constitutive laws is crucial for accurately predicting the mechanical behavior of materials under complex loading conditions. Accurate model calibration hinges on a delicate interplay between the information embedded in experimental data and the parameters that define our constitutive models.The information encoded in the parameters of the constitutive model must be complemented by the information in the data used for calibration. This interplay raises fundamental questions: How can we quantify the information content of test data? How much information does a single test convey? Also, how much information is required to accurately learn a constitutive model? To address these questions, we introduce mechanics informatics, a paradigm for efficient and accurate constitutive model learning. At its core is the stress state entropy, a metric for quantifying the information content of experimental data. Using this framework, we analyzed specimen geometries with varying information content for learning an anisotropic inelastic law. Specimens with limited information enabled accurate identification of a few parameters sensitive to the information in the data. Furthermore, we optimized specimen design by incorporating stress state entropy into a Bayesian optimization scheme. This led to the design of cruciform specimens with maximized entropy for accurate parameter identification. Conversely, minimizing entropy in Peirs shear specimens yielded a uniform shear stress state, showcasing the framework's flexibility in tailoring designs for specific experimental goals. Finally, we addressed experimental uncertainties, demonstrated the potential of transfer learning for replacing challenging testing protocols with simpler alternatives, and extension of the framework to different material laws.

cs.CE↗

arXiv2025

Line Tension Reshapes Nucleation at Surface Edges: A Generalized Theory for Nanopore Activation

Heterogeneous nucleation at surface edges is pervasive across nature and industry, yet the role of line tension, arising from asymmetric capillary interactions at geometric singularities, remains poorly understood. Herein we develop a generalized nucleation theory that explicitly incorporates line tension induced by edge pinning, thereby extending classical frameworks to account for nanoscale confinement and interfacial asymmetry. Through analytical treatment of droplet formation within geometrically defined nanopores, we derive a closed-form expression for the edge-pinned line tension as a function of Laplace pressure, pore geometry, and wettability. This formulation reveals that line tension can significantly reshape the nucleation energy landscape, introducing nontrivial dependencies on contact angle and pore morphology. Our results uncover a tunable, geometry-mediated mechanism for controlling nucleation barriers, offering predictive insight into phase transitions in confined environments and suggesting new strategies for design in applications ranging from nanofluidics to crystallization control.

cond-mat.soft↗

arXiv2025

Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics

Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and process insights. Lastly, the use of the models to guide development of advanced process control and optimization strategies is discussed.

cond-mat.soft↗

arXiv2025

Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles

The Doyle-Fuller-Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity within the DFN model are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode's diffusion coefficients and reaction rate constants of 95 Tesla Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide--graphite (LiC$_\text{6}$--SiO$_{\text{x}}$) anode. The parameters are estimated at intermittent diagnostic cycles over the lifetime of each cell. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for uncertainty quantification (UQ) for a total of 7776 cycles at discharge C-rates of C/5, 1C, and 2C. While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the parameters contributing to cell aging. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions.

eess.SY↗

arXiv2025

Accelerating and enhancing thermodynamic simulations of electrochemical interfaces

Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly $\textit{ab initio}$ sampling, and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO$_\mathrm{3}$(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications.

cond-mat.mtrl-sci↗

arXiv2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery's current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery's degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. In this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning. We demonstrate the versatility of this model framework by applying it to three battery-cycling datasets consisting of 422 cells under different operating conditions, highlighting the utility of an interpretable diagnostic-free, onboard battery diagnosis and prognosis model.

eess.SY↗

arXiv2025

Theory of Cation Solvation in the Helmholtz Layer of Li-ion Battery Electrolytes

The solvation environments of Li$^+$ in conventional non-aqueous battery electrolytes, such as LiPF$_6$ in mixtures of ethylene carbaronate (EC) and ethyl methyl carbonate (EMC), are often used to rationalize the transport properties of electrolytes and solid electrolyte interphase (SEI) formation. In the SEI, the solvation environments in the compact electrical double layer (EDL) next to the electrode, also known as the Helmholtz layer, determine (partially) what species can react to form the SEI, with bulk solvation environments often being used as a proxy. Here we develop and test a theory of cation solvation in the Helmholtz layer of non-aqueous Li-ion battery electrolytes. First, we validate the theory against bulk and diffuse EDL atomistic molecular dynamics (MD) simulations of LiPF$_6$ EC/EMC mixtures as a function of surface charge, where we find the theory can capture the solvation environments well. Next we turn to the Helmholtz layer, where we find that the main effect of the solvation structures next to the electrode is an apparent reduction in the number of binding sites between Li$^+$ and the solvents, again where we find good agreement with our developed theory. Finally, by solving a simplified version of the theory, we find that the probability of Li$^+$ binding to each solvent remains equal to the bulk probability, suggesting that the bulk solvation environments are a reasonable place to start when understanding new battery electrolytes. Our developed formalism can be parameterized from bulk MD simulations and used to predict the solvation environments in the Helmholtz layer, which can be used to determine what could react and form the SEI.

cond-mat.stat-mech↗

arXiv2025

Potential of mean force and underscreening of polarizable colloids in concentrated electrolytes

This study uses advanced numerical methods to estimate the mean force potential (PMF) between charged, polarizable colloidal particles in dense electrolytes. We observe that when the Debye screening length, $λ_{\mathrm{D}}$, is below the hydrated ion size, the PMF shows discernible oscillations of purely electrostatic origin as opposed to chemical affinity, in addition to the expected decay in DLVO theory. Moreover, our findings suggest concentrated electrolytes are significantly less efficient at muting electrostatic interactions in electrostatically stabilized colloidal suspensions, potentially having significant implications for our understanding of colloidal stability and the forces that govern the behavior of concentrated charged soft matter systems beyond DLVO theory.

cond-mat.soft↗