Electro-Chemo-Mechanical Modeling of Multiscale Active Materials for Next-Generation Energy Storage: Opportunities and Challenges

The recent geopolitical crisis resulted in a gas price surge. 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.Comment: 33 pages, 17 figure

Exploring the Mechanical Behaviors of 2D Materials in Electrochemical Energy Storage Systems: Present Insights and Future Prospects

2D materials (2DM) and their heterostructures (2D + nD, n = 0,1,2,3) hold significant promise for applications in Electrochemical Energy Storage Systems (EESS), such as batteries. 2DM can serve as van der Waals (vdW) slick interface between conventional active materials (e.g., Silicon) and current collectors, modifying interfacial adhesion and preventing stress-induced fractures. Additionally, 2DM can replace traditional polymer binders (e.g., MXenes). This arrangement also underscores the critical role of interfacial mechanics between 2DM and active materials. Furthermore, 2DM can be designed to function as an electrode itself. For instance, a porous graphene network has been reported to possesses approximately five times the capacity of a traditional graphite anode. Consequently, gaining a comprehensive understanding of the mechanical properties of 2DM in EESS is paramount. However, modeling 2DM in EESS poses significant challenges due to the intricate coupling of mechanics and electrochemistry. For instance, defective graphene tends to favor adatom adsorption (e.g., Li+) during charging. In cases of strong adsorption, adatoms may not readily detach from electrodes during discharging. As a result, in such scenarios, adsorption-desorption (charge-discharge) processes govern the mechanical properties of 2DM when used as binders and current collectors. Regrettably, most existing studies on the mechanical properties of 2DM in EESS have failed to adequately address these critical issues. This perspective paper aims to provide a comprehensive overview of recent progress in the chemo-mechanics of 2DM's mechanical properties. A wide spectrum of multiscale modeling approaches, including atomistic/molecular simulations, continuum modeling, and machine learning, are discussed.Comment: 49 pages, 33 figure

Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries

Data-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si) - alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy and packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced.Comment: 23 pages, 7 figure

Variation in interface strength of Silicon with surface engineered Ti3C2 MXenes

Current advancements in battery technologies require electrodes to combine high-performance active material 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 Ti3C2Tx MXene are determined as the MXene surface functional groups (Tx) are changed using first-principle calculations. Si is interfaced with three Ti3C2 MXene substrates having surface -OH, -OH and -O mixed, and -F functional groups. Density functional theory (DFT) results reveal that completely hydroxylated Ti3C2 has the highest interface strength of 0.563 J/m2 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 physiochemical factors affecting the surface chemistry and resultant interface strength values. The presented comprehensive analysis of the interface aims to aid in developing sophisticated MXene based electrodes by their targeted surface engineering.Comment: 21 pages with 4 figures and. 3 tabl

Effect of Graphene Interface on Potassiation in a Graphene- Selenium Heterostructure Cathode for Potassium-ion Batteries

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 KxSe cathode enclosed in hexagonal lattices of carbon(C) characteristic of multilayered graphene matrix and multiwalled carbon nanotubes (MW-CNTs). Microstructural changes directed by graphene substrate in KxSe cathode are contrasted with graphene-free cathode. Graphene's binding affinity for long-chain polyselenides (Se-Se-Se = -2.82 eV and Se-Se = -2.646 eV) and ability to induce reactivity between Se and K are investigated. Furthermore, intercalation voltage for graphene enclosed KxSe cathode reaction intermediates are 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. Our findings suggest that operating at higher voltages (~2V) 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. Primary issues are the high binding energy of long-chain polyselenides with graphene that discourage K storage and Se-Se bond dissociation at low K concentrations. A comparison with graphene-free cathode highlights the substantial changes a van der Waals (vdW) graphene interface can bring in atomic-structure and electrochemistry of the KxSe cathode.Comment: 7 Figures and 1 Tabl