BN-based materials in modern electronics, quantum photonics, and energy devices. Despite their technological importance, the atomistic mechanisms underlying its phase evolution remain poorly understood. Here, we use large-scale molecular dynamics simulations with a MACE-based machine-learning interatomic potential to investigate phase-transformation pathways in crystalline BN. Trained on high-fidelity density functional theory (DFT) data and validated against ab initio molecular dynamics simulations, the potential enables simulations of phase evolution across extended length and time scales. We identify a layer-by-layer transformation mechanism that initiates at interfaces, where lattice mismatch and local structural disorder promote reconstructive transformations. We further show that transformation propensity depends non-monotonically on interfacial energy and is enhanced by compressive stresses generated during thermal treatment. These results provide atomistic insight into BN phase transformations under diverse thermodynamic conditions, while the associated MACE training workflow offers a generalizable framework for studying phase-transformation mechanisms in other complex materials.

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. This review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing-structure-property-performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. To address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure-transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Electrochemical stability windows determine electrolyte operating limits, yet stability arises from statistical ensembles of local solvation environments rather than isolated structures. We capture this by coupling MD with solvation-aware GNNs.

Sodium-ion batteries are a cost-effective and sustainable alternative to lithium-ion systems for large-scale energy storage. Hard carbon (HC) anodes, composed of disordered graphitic and amorphous domains, offer high capacity but exhibit complex, poorly understood ion transport behavior. In particular, the relationship between local microstructure and sodium mobility remains unresolved, hindering rational performance optimization. Here, we introduce a data-driven framework that combines machine-learned interatomic potentials with molecular dynamics simulations to systematically investigate sodium diffusion across a broad range of carbon densities and sodium loadings. By computing per-ion structural descriptors, we identify the microscopic factors that govern ion transport. Unsupervised learning uncovers distinct diffusion modes, including hopping, clustering, and void trapping, while supervised analysis highlights tortuosity and NaNa coordination as primary determinants of mobility. Correlation mapping further connects these transport regimes to processing variables such as bulk density and sodium content. This physics-informed approach establishes quantitative structure-transport relationships that capture the heterogeneity of disordered carbon. Our findings deliver mechanistic insights into sodium-ion dynamics and provide actionable design principles for engineering high-performance HC anodes in next-generation battery systems.

Copper-based nanoparticles are key electrocatalysts for CO2 electrochemical reduction (CO2 ER) to liquid fuels and other value-added products. However, the copper catalyst can undergo rapid electrochemical corrosion, leading to a loss of catalyst material, fluctuations in the reaction conditions and increasing operational costs. We establish a mechanistic understanding of this detrimental process using in situ electrochemical electron microscopy and density functional theory (DFT). We find that copper corrosion can occur in the presence of CO2 in electroless conditions and before the onset potentials required for CO2 ER. The effects are isolated from pH changes resulting from dissolved CO2. Particles of corroded copper have oxidized surfaces, in contrast to copper surfaces exposed to CO2-free electrolytes. DFT calculations identify multiple routes by which CO2 can behave as a dissolution agent for copper and copper-oxide surfaces and suggest that formate-intermediates are a key driver of corrosion. This study highlights microenvironment-based factors that affect copper performance and degradation, facilitating strategies to inhibit and reverse copper degradation during CO2 ER.

Electrochemical interfaces are critical to the performance and durability of lithium-ion batteries (LIBs). The solid electrode-electrolyte interphase (SEI and CEI) structures that form during cycling can passivate reactive surfaces, ensuring safe operation, but also may contribute to performance degradation. Understanding the microscopic factors influencing interphase formation, growth, and evolution is essential for balanced battery design. While significant research has focused on the anode-electrolyte interphase (SEI), the cathode-electrolyte interphase (CEI) remains less explored, despite its importance in high-voltage and advanced battery technologies. Challenges in conducting in-situ or operando experiments arise from the occluded nature of these interfaces and the long timescales involved, often leading to biased interpretations. A validated multi-scale, multi-physics modeling approach, integrated with advanced characterization techniques, can effectively elucidate the intrinsic stability of electrolyte and cathode surfaces, the impact of chemical heterogeneity, and the role of microstructural features on CEI performance. This article reviews current modeling and simulation strategies for studying CEI in advanced LIBs and highlights opportunities for future methodological advancements and experimental integration.

High-Ni content NMC are promising cathode materials for next-generation high voltage Li-ion batteries, however, their practicality is limited by significant capacity fade during cycles due to interfacial reactivity to the electrolyte and undesired phase evolution at highly charged states. The issues associated with their interfacial instability can be further augmented by cracking and fracturing behavior upon cycling such that various degree of degradation can occur, leading to the formation of so-called cathode/electrolyte interphases (CEI). To maintain desired functionality and performance of the battery, it is crucial that this dynamically evolving CEI is self-contained and does not considerably impede Li-ion transport. In this talk, we elucidate from atomistic simulations how the unique surface structure and chemistry dictates the reactivity of high-Ni content NMC cathode against common electrolyte components. We further emphasize the importance of addressing the structural and chemical inhomogeneity of CEI and its impact on Li-ion transport behavior. The mechanistic understanding obtained in this study can provide valuable insights towards the design of functional CEI for extended cycling life of NMC cathodes. This work is sponsored by the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office and is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Na-ion batteries offer promising energy storage solution for large-scale deployment given its earth-abundant nature, low cost, improved safety feature, and resilience to domestic supply chain issues. To date, hard carbon (HC) remains the optimal choice as the anode material due to its balanced performance-cost tradeoff for commercialization. In addition, HC can be sourced from a wide variety of biowastes and are versatile to both conventional and advanced processing and fabrication techniques, making their microstructure and storage capacity, reversibly highly tunable. However, for the same reason, their performance metrics such as initial coulombic efficiency, specific and rate capacities can be strongly dictated by their microstructures resulting from different preparation and pre-treatment processes. Without a comprehensive understanding of the processing-structure-property relationship, tremendous trial-and-error efforts and laboratory exploration are needed, which significantly impede the advancement, quality control and maturity of Na-ion battery technology for large-scale implementation. In this talk, I will demonstrate how machine-learning assisted atomistic modeling can help to elucidate various competing Na storage mechanisms in hard carbon and their correlation to common microstructural features, such as randomly oriented graphitic and amorphous nano-domains, defective regions of different sizes, nanovoids/pores. Local Na diffusivities will be extracted to provide guidance of transport and storage optimization via microstructure tuning. Ultimately, insights obtained in these atomistic simulations will allow rational design of low-cost hard carbon anode with high Na storage capacity, enhanced rate performance and prolonged cycling life. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Lithium-ion batteries (LIBs) are central to the urgent societal need to decarbonize both transportation and energy storage on the grid. Unfortunately, despite their attractive energy/power density, as well as high coulombic and energy efficiencies, further improvement of this technology - especially their durability - is desperately needed. To support these efforts, our study focuses on fundamental understanding of the decomposition pathways for LIB electrolytes at the cathode-electrolyte interface (CEI), as the nature of these reactions directly controls the extent to which cell capacity and voltage decays in these systems. In this study, we employ electrochemical methods, coupled with product analysis using NMR spectroscopy and mass spectrometry, to determine the decomposition mechanisms in both model and technologically relevant electrolytes. Remarkably, we discovered the electrochemical formation of protons with high chemical activity, comparable to known superacids, at potentials relevant to practical Li-ion batteries. Their reactivity toward every individual component of the CEI provides a unified thermochemical origin for a myriad of side reactions that are commonly associated with the electrochemical reaction. In particular, electrochemically generated protons react with intact EC molecules to form CO2 and other short and long chain ethers. They also undergo an acid-base reaction with LiPF6, to form the weaker acid HF, and with the cathode active material, leaching transition metals into the electrolyte. Collectively, the results of this study all point to the urgent need to either mitigate this proton formation or introduce benign harvesting additives via new electrolyte design strategies.

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the known molecules seen in training. We find that a key limitation is not in the molecule generation process itself, but in the poor generalization capabilities of molecular property predictors. We tackle this challenge by creating an active-learning, closed-loop molecule generation pipeline, whereby molecular generative models are iteratively refined on feedback from quantum chemical simulations to improve generalization to new chemical space. Compared against other generative model approaches, only our active learning approach generates molecules with properties that extrapolate beyond the training data (reaching up to 0.44 standard deviations beyond the training data range) and out-of-distribution molecule classification accuracy is improved by 79 stability data from the active-learning loop, the proportion of stable molecules generated is 3.5x higher than the next-best model.

Transition metal oxides (TMOs), such as LiNiO2, are promising candidates for energy storage and electronic devices due to their unique electronic properties, exceptional physical and chemical characteristics, and ability to adopt multiple oxidation states. However, accurately predicting their properties using mean-field density functional theory (DFT) is challenging due to the presence of strongly correlated d-electrons and the complex interplay between their structural, electronic, and magnetic responses. These challenges are further exacerbated by the need to model defects, surfaces, and interfaces, which require computationally efficient, large-scale simulations. To address these issues, we carry out a benchmark study on the Li1-x NiO2 system, evaluating the performance of several popular functionals. Our findings demonstrate that combining SCAN functional relaxation with single-step HSE calculations provides a practical and scalable computational strategy. This approach balances accuracy and efficiency, enabling high-throughput simulations of strongly correlated TMOs and improved predictive modeling capability of TMOs for practical applications.

Sustainable energy storage is essential to support the transition to renewables and meet the increasing demand for energy. Sodium-ion batteries (NIBs) are attractive for grid-scale energy storage due to the abundance and low cost of sodium, sustainability of other battery components, and electrochemical performance. Hard carbon (HC) is a leading anode material for NIBs, but its complex microstructure complicates the understanding of sodium storage mechanisms. Using X-ray total scattering and density functional theory calculations, this study clarifies how HC's microstructural variations influence sodium storage across the slope (high potential) and plateau (low potential) regions of the potential capacity curve. In the slope region, sodium initially adsorbs at high-binding energy defect sites and subsequently intercalates between graphene layers, adsorbing at low-binding energy defect sites, correlating with different slopes observed during initial sodiation. Initial irreversibility arises from sodium trapping at surface defects and solid electrolyte interface formation. In the plateau region, sodium simultaneously intercalates and fills pores, influenced by pore size, interlayer spacing, and defect concentration. HCs with larger pore sizes form larger sodium clusters. The proposed mechanism underscores the role of microstructure engineering in enhancing HC performance and advancing NIBs for grid-scale energy storage.

It is well recognized that the performance of commercial Li-ion batteries can significantly degrade at low temperatures due to sluggish ion transport in bulk electrolyte, increased resistance at interfaces and severe Li plating and dendrite growth that could lead to catastrophic cell failure. In this talk, we address the low temperature performance constraints of the electrolyte components from atomistic modeling and demonstrate the critical importance to balance viscosity and ionic speciation that dictate the overall conductivity of Li-ion in model electrolyte. We further discuss how speciation in the bulk electrolyte could affect Li-ion desolvation pathways and associated kinetics at interfaces. Through rigorous data analysis, we resolve species-specific transport properties of the electrolyte, its correlation to temperature and concentration, and provide valuable insights for future design of functional electrolytes for low temperature applications. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by the Laboratory Directed Research and Development program under the tracking number of 23-SI-005.

Understanding the behavior of gas within confined ionic liquids (ILs) is important for a wide range of emerging energy, separation, and sensing technologies. However, the mechanisms governing gas solubility and molecular structure within these systems remain largely unknown. Here, we investigate the factors that dictate the intercalation and arrangement of CO2, N2 and O2, in a commonly used IL (1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM+][PF6-]) confined within neutral and charged 2.1 nm diameter carbon nanotubes (CNTs) via molecular dynamics simulations and enhanced free energy sampling methods. Our simulations show that the gas selectivity in these systems can be explained by a competitive complex interplay between confinement, charge state of CNTs, and IL properties. We then experimentally validate a subset of these predictions using a novel device consisting of electrically addressable, IL-infilled CNTs which we expose to CO2 and O2 in a N2 background. Our findings help to disentangle the relative importance of tuning gas solubility and preferential proximity to the CNT wall for maximizing measurable changes of electrochemical signals. These insights provide a foundation for engineering future electrochemical systems utilized in gas sensing or separation applications.

This study revisits the three-point sampling of the simplified Butler-Volmer equation to address the limitations of strong potentiodynamic polarization, which can introduce irreversible damage and uncertainty in corrosion analysis. The method extracts electrochemical kinetic parameters while minimizing polarization effects, evaluates noise sensitivity relative to overpotential, and accounts for errors from signal noise, OCP drift, ohmic resistance, and mass-transfer constraints. Verified against the Tafel extrapolation method for aluminum corrosion across a wide pH range, this low-polarization approach enables accurate evaluations with specific error estimates, offering a robust alternative to linear polarization resistance methods that assume constant Tafel slopes.

Electrochemical energy storage (EES) and conversion devices (e.g. batteries, supercapacitors, and reactors) are emerging as primary methods for global efforts to shift energy dependence from limited fossil fuels towards sustainable and renewable resources. These devices, while showing great potential for meeting some key metrics set by conventional technologies, still face significant limitations. For example, an EES device tends to exhibit large energy density (e.g. lithium-ion battery) or power density (e.g. supercapacitor), but not both. This inability of a single device to simultaneously achieve both metrics represents a major obstacle to widespread adoption of EES devices. Similarly, many catalytic processes depend on expensive platinum group metals (PGM) because non-PGM materials have poor performance or durability. Though the integration of 2D materials (e.g. graphene, dichalcogenides, MXene, etc.) into electrochemical devices has yielded some exciting results towards tackling these issues, significant improvements are still needed. One approach to optimizing the performance of these devices is to focus on one of the fundamental processes that occur in these systems: mass (or charge) transport. The efficient transport of ions within EES and conversion devices is critical to realizing better performance and durability. The pore structure of the electrode is a key factor in determining this transport phenomena, but in many cases, engineering the pore structure in a highly deterministic fashion can be challenging. In this work, we explore a number of additive manufacturing methods (e.g. direct ink write, projection microstereolithography, etc.) to engineer the pore structure of device electrodes. We also determine effective electrode geometries using both simple theory and topology optimization techniques. The topology optimization couples the solution of the forward electrochemical problem over the full electrode domain with gradient-based optimization. The output of our code is a three-dimensional CAD representation which optimizes over specific performance metrics and which can be used to print functional electrodes. This work provides a systematic path toward automated design and fabrication of engineered electrodes with precise control over the fluid and species distribution. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Lithium-ion batteries (LIBs) have become a core portable energy storage technology due to their high energy density, longevity, and affordability. Nevertheless, their use in low-temperature environments is challenging due to significant Li-metal plating and dendrite growth, sluggish Li-ion desolvation kinetics, and suppressed Li-ion transport. In this study, we employ classical molecular dynamics simulations to provide a mechanistic understanding of the impact of temperature- and concentration-effects on the ionic conductivity of a prototypical battery electrolyte, lithium hexafluorophosphate in ethylene carbonate (LiPF6/EC). We further investigate the interplay between temperature and ionic speciation via a graph-based clustering analysis that resolves species-specific ionic conductivity contributions. Using these findings, we formulate two fundamental design principles governing electrolyte performance: one for ambient temperature and another for low-temperature conditions. The modeling framework outlined in this work provides a foundation for identifying design principles that can be used to rationally improve the low-temperature performance of LIBs. Formulating and establishing design principles to improve low-temperature performance of battery electrolytes.

The cathode–electrolyte interphase plays a pivotal role in determining the usable capacity and cycling stability of electrochemical cells, yet it is overshadowed by its counterpart, the solid–electrolyte interphase. This is primarily due to the prevalence of side reactions, particularly at low potentials on the negative electrode, especially in state-of-the-art Li-ion batteries where the charge cutoff voltage is limited. However, as the quest for high-energy battery technologies intensifies, there is a pressing need to advance the study of cathode–electrolyte interphase properties. Here, we present a comprehensive approach to analyse the cathode–electrolyte interphase in battery systems. We underscore the importance of employing model cathode materials and coin cell protocols to establish baseline performance. Additionally, we delve into the factors behind the inconsistent and occasionally controversial findings related to the cathode–electrolyte interphase. We also address the challenges and opportunities in characterizing and simulating the cathode–electrolyte interphase, offering potential solutions to enhance its relevance to real-world applications. The cathode–electrolyte interphase (CEI) is vital for battery cell capacity and stability but receives less attention than the solid–electrolyte interphase. The authors review CEI properties, emphasize using model cathode materials and coin cell protocols, and address challenges and opportunities in characterizing and simulating CEI for real-world applications.

The activity and product selectivity of electrocatalysts for reactions like the carbon dioxide reduction reaction (CO2RR) are intimately dependent on the catalyst's structure and composition. While engineering catalytic surfaces can improve performance, discovering the key sets of rational design principles remains challenging due to limitations in modeling catalyst stability under operating conditions. Herein, we perform first-principles density functional calculations adopting implicit solvation methods with potential control to study the influence of adsorbates and applied potential on the stability of different facets of model Cu electrocatalysts. Using coverage dependencies extracted from microkinetic models, we describe an approach for calculating potential and adsorbate-dependent contributions to surface energies under reaction conditions, where Wulff constructions are used to understand the morphological evolution of Cu electrocatalysts under CO2RR conditions. We identify that CO*, a key reaction intermediate, exhibits higher kinetically and thermodynamically accessible coverages on (100) relative to (111) facets, which can translate into an increased relative stabilization of the (100) facet during CO2RR. Our results support the known tendency for increased (111) faceting of Cu nanoparticles under more reducing conditions and that the relative increase in (100) faceting observed under CO2RR conditions is likely attributed to differences in CO* coverage between these facets.