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Lithium-ion batteries (LIBs) are ubiquitous in everyday applications. However, Lithium (Li) is a limited resource on the planet and, therefore, not sustainable. As an alternative to lithium, earth-abundant and cheaper multivalent metals such as aluminum (Al) and calcium (Ca) have been actively researched in battery systems. However, finding suitable intercalation hosts for multivalent-ion batteries is urgently needed. Open-tunneled oxides represent a specific category of microparticles distinguished by the presence of integrated one-dimensional channels or nanopores. This work focuses on two promising open-tunnel oxides: Niobium Tungsten Oxide (NTO) and Molybdenum Vanadium Oxide (MoVO). The MoVO structure can accommodate a larger number of multivalent ions than NTO due to its larger surface area and different shapes. Specifically, the MoVO structure can adsorb Ca, Li, and Al ions with adsorption potentials ranging from around 4 to 5 eV. However, the adsorption potential for hexagonal channels of Al ion drops to 1.73 eV due to the limited channel area. The NTO structure exhibits an insertion/adsorption potential of 4.4 eV, 3.4 eV, and 0.9 eV for one Li, Ca, and Al, respectively. Generally, Ca ions are more readily adsorbed than Al ions in both MoVO and NTO structures. Bader charge analysis and charge density plots reveal the role of charge transfer and ion size in the insertion of multivalent ions such as Ca and Al into MoVO and NTO systems. Exploring open-tunnel oxide materials for battery applications is hindered by vast compositional possibilities. The execution of experimental trials and quantum-based simulations is not viable for addressing the challenge of locating a specific item within a large and complex set of possibilities. Therefore, it is imperative to conduct structural stability testing to identify viable combinations with sufficient pore topologies. Data mining and machine learning techniques are employed to discover innovative transitional metal oxide materials. This study compares two machine learning algorithms, one utilizing descriptors and the other employing graphs to predict the synthesizability of new materials inside a laboratory setting. The outcomes of this study offer valuable insights into the exploration of alternative naturally occurring multiscale particles. 

Although lithium-ion batteries represent the best available rechargeable battery technology, a significant energy and power density gap exists between LIBs and petrol/gasoline. The battery electrodes comprise a mixture of active materials particles, conductive carbon, and binder additives deposited onto a current collector. Although this basic design has persisted for decades, the active material particle’s desired size scale is debated. Traditionally, microparticles have been used in batteries. Advances in nanotechnology have spurred interest in deploying nanoparticles as active materials. However, despite many efforts in nano, industries still primarily use ‘old’ microparticles. Most importantly, the battery industry is unlikely to replace microstructures with nanometer-sized analogs. This poses an important question: Is there a place for nanostructure in battery design due to irreplaceable microstructure? The way forward lies in multiscale active materials, microscale structures with built-in nanoscale features, such as microparticles assembled from nanoscale building blocks or patterned with engineered or natural nanopores. Although experimental strides have been made in developing such materials, computational progress in this domain remains limited and, in some cases, negligible. However, the fields hold immense computational potential, presenting a multitude of opportunities. This perspective highlights the existing gaps in modeling multiscale active materials and delineates various open challenges in the realm of electro-chemo-mechanical modeling. By doing so, it aims to inspire computational research within this field and promote synergistic collaborative efforts between computational and experimental researchers. 

Selenium (Se) cathodes are an exciting emerging high energy density storage system for potassium-ion batteries (KIB), where potassiation reactions are less understood. Here, we present an atomic-level investigation of a KxSe cathode enclosed in hexagonal lattices of carbon (C) characteristic of a layered graphene matrix and multiwalled carbon nanotubes (MW-CNTs). Microstructural changes directed by the graphene–substrate in the KxSe cathode are contrasted with those in the graphene-free cathode. Graphene’s binding affinity for long-chain polyselenides (Se3 = −2.82 eV and Se2 = −2.646 eV) at low K concentrations and ability to induce enhanced reactivity between Se and K at high K concentrations are investigated. Furthermore, intercalation voltage for graphene-enclosed KxSe cathode reaction intermediates is calculated with K2Se as the final discharged product. Our results indicate a single-step reaction near a voltage of 1.55 V between K and Se cathode. Findings in the paper suggest that operating at higher voltages (∼2 V) could result in the formation of reaction intermediates where intercalation/deintercalation of K could be a challenge, and therefore cause irreversible capacity losses in the battery. The primary issue here is the modulating favorability of graphene surface toward discharging of Se cathode due to its differential preferences for K–Se reaction intermediates. A comparison with a graphene-free cathode highlights the substantial changes a van der Waals (vdW) graphene interface can bring in the atomic structure and electrochemistry of the KxSe cathode. 

Two-dimensional materials (e.g., graphene and transition metal dichalcogenides) and their heterostructures have enormous applications in electrochemical energy storage systems such as batteries. A comprehensive and solid understanding of these materials’ thermal transport and mechanism is essential for practical device design. Several advanced experimental techniques have been developed to measure the intrinsic thermal conductivity of materials. However, experiments have challenges in providing improved control and characterization of complex structures, especially for low-dimensional materials. Theoretical and simulation tools, such as first-principles calculations, Boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and nonequilibrium Green’s function, provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations requires high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties requires novel computational approaches. The machine learning method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and machine learning of thermal transport in two-dimensional materials. The paper also addresses the latest significant experimental advances. However, designing the best two-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bilayer and multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part of this review addresses the future research direction in two-dimensional materials-based heterostructure design for thermal transport in energy storage systems. 

Abstract Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability. 

Abstract The development of next-generation batteries, utilizing electrodes with high capacities and power densities requires a comprehensive understanding and precise control of material interfaces and architectures. Electro-chemo-mechanics plays an integral role in the morphological evolution and stability of such complex interfaces. Volume changes in electrode materials and the chemical interactions of electrode/electrolyte interfaces result in nonuniform stress fields and structurally different interphases, fundamentally affecting the underlying transport and reaction kinetics. The origin of this mechanistic coupling and its implications on degradation is uniquely dependent on the interface characteristics. In this review, the distinct nature of chemo–mechanical coupling and failure mechanisms at solid–liquid interfaces and solid–solid interfaces is analyzed. For lithium metal electrodes, the critical role of surface/microstructural heterogeneities on the solid electrolyte interphase (SEI) stability and dendrite growth in liquid electrolytes, and on the onset of contact loss and filament penetration with solid electrolytes is summarized. With respect to composite electrodes, key differences in the microstructure-coupled electro-chemo-mechanical attributes of intercalation- and conversion-based chemistries are delineated. Moving from liquid to solid electrolytes in such cathodes, we highlight the significant impact of solid–solid point contacts on transport/mechanical response, electrochemical performance, and failure modes such as particle cracking and delamination. Finally, we present our perspective on future research directions and opportunities to address the underlying electro-chemo-mechanical challenges for enabling next-generation lithium metal batteries. 

Current advancements in battery technologies require electrodes to combine high-performance active materials such as Silicon (Si) with two-dimensional materials such as transition metal carbides (MXenes) for prolonged cycle stability and enhanced electrochemical performance. More so, it is the interface between these materials, which is the nexus for their applicatory success. Herein, the interface strength variations between amorphous Si and Ti 3 C 2 T x MXenes are determined as the MXene surface functional groups ( T x ) are changed using first principles calculations. Si is interfaced with three Ti 3 C 2 MXene substrates having surface −OH, −OH and −O mixed, and −F functional groups. Density functional theory (DFT) results reveal that completely hydroxylated Ti 3 C 2 has the highest interface strength of 0.6 J m −2 with amorphous Si. This interface strength value drops as the proportion of surface −O and −F groups increases. Additional analysis of electron redistribution and charge separation across the interface is provided for a complete understanding of underlying physico-chemical factors affecting the surface chemistry and resultant interface strength values. The presented comprehensive analysis of the interface aims to develop sophisticated MXene based electrodes by their targeted surface engineering. 

Lithium metal–selenium (Li–Se) batteries offer high volumetric energy but are limited in their cycling life and fast charge characteristics. Here a facile approach is demonstrated to synthesize hierarchically porous hollow carbon spheres that host Se (Se@HHCS) and allow for state-of-the-art electrochemical performance in a standard carbonate electrolyte (1 M LiPF 6 in 1 : 1 EC : DEC). The Se@HHCS electrodes display among the most favorable fast charge and cycling behavior reported. For example, they deliver specific capacities of 442 and 357 mA h g −1 after 1500 and 2000 cycles at 5C and 10C, respectively. At 2C, Se@HHCS delivers 558 mA h g −1 after 500 cycles, with cycling coulombic efficiency of 99.9%. Post-mortem microstructural analysis indicates that the structures remain intact during extended cycling. Per GITT analysis, Se@HHCS possesses significantly higher diffusion coefficients in both lithiation and delithiation processes as compared to the baseline. The superior performance of Se@HHCS is directly linked to its macroscopic and nanoscale pore structure: the hollow carbon sphere morphology as well as the remnant open nanoporosity accommodates the 69% volume expansion of the Li to Li 2 Se transformation, with the nanopores also providing a complementary fast ion diffusion path. 

We present comprehensive first-principles density functional theory (DFT) analyses of the interfacial strength and bonding mechanisms between crystalline and amorphous selenium (Se) with graphene (Gr), a promising duo for energy storage applications. Comparative interface analyses are presented on amorphous silicon (Si) with graphene and crystalline Se with a conventional aluminum (Al) current collector. The interface strengths of monoclinic Se (0.43 J m–2) and amorphous Si with graphene (0.41 J m–2) are similar in magnitude. While both materials (c-Se, a-Si) are bonded loosely by van der Waals (vdW) forces over graphene, interfacial electron exchange is higher for a-Si/graphene. This is further elaborated by comparing the potential energy step and charge transfer (Δq) across the graphene interfaces. The interface strength of c-Se on a 3D Al current collector is higher (0.99 J m–2), suggesting a stronger adhesion. Amorphous Se with graphene has comparable interface strength (0.34 J m–2), but electron exchange in this system is slightly distinct from monoclinic Se. The electronic characteristics and bonding mechanisms are different for monoclinic and amorphous Se with graphene as they activate graphene via surface charge doping divergently. The implications of these interfacial physicochemical attributes on electrode performance have been discussed. Our findings highlight the complex electrochemical phenomena in Se interfaced with graphene, which may profoundly differ from their “free” counterparts.