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The development of next-generation energy storage systems relies on discovering new materials that support multivalent-ion transport. Transition metal oxides (TMOs) are promising due to their structural versatility, high ionic conductivity, and ability to accommodate multiple charge carriers. However, their vast compositional and structural diversity makes traditional exploration inefficient. This work presents a generative AI framework combining a crystal diffusion variational autoencoder (CDVAE) and a fine-tuned large language model (LLM) to discover porous oxide materials. Thousands of candidate structures are generated and screened for structural validity, thermodynamic stability, and electronic properties using a graph-based machine learning model and density functional theory (DFT) calculations. CDVAE identifies a broader variety of structures, including five novel TMO-based candidates, while LLM excels in generating highly stable structures near equilibrium. This approach demonstrates the power of generative AI in accelerating the discovery of advanced battery materials for multivalent-ion storage. 

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. 

Using a syringe pump setup, the authors conducted water flow experiments through porous reduced graphene oxide (rGO). A variety of anions and cations were added to the water to study its effect on energy harvesting. More specifically, the authors performed tests to study effect of: (1) ion concentration in water, (2) type of anion used, (3) type of cation used, and (4) effect of flow rate. The test data indicates that water flow through rGO networks can directly induce drift of charge carriers in graphene and thus generate electricity. Graphene is ideally suited for this application, since it possesses high mobility charge carriers that are ready to be coupled to moving ions present in the flowing fluid. The proposed rGO material could enable harvesting of the ubiquitous, abundant, and renewable mechanical energy of moving water directly to electrical energy. Unlike traditional schemes, the graphene material directly converts the flow energy into electrical energy without the need for moving parts. Such graphene coatings could potentially replace conventional batteries (which are environmentally hazardous) in low‐power, low‐voltage, and long service‐life applications. Once scaled up, this concept offers a potentially transformative approach to energy harvesting, as opposed to incremental advances in current technologies. 

Abstract Piezoelectric materials show potential to harvest the ubiquitous, abundant, and renewable energy associated with mechanical vibrations. However, the best performing piezoelectric materials typically contain lead which is a carcinogen. Such lead-containing materials are hazardous and are being increasingly curtailed by environmental regulations. In this study, we report that the lead-free chalcogenide perovskite family of materials exhibits piezoelectricity. First-principles calculations indicate that even though these materials are centrosymmetric, they are readily polarizable when deformed. The reason for this is shown to be a loosely packed unit cell, containing a significant volume of vacant space. This allows for an extended displacement of the ions, enabling symmetry reduction, and resulting in an enhanced displacement-mediated dipole moment. Piezoresponse force microscopy performed on BaZrS₃confirmed that the material is piezoelectric. Composites of BaZrS₃particles dispersed in polycaprolactone were developed to harvest energy from human body motion for the purposes of powering electrochemical and electronic devices. 

Driven by the cost and scarcity of Lithium resources, it is imperative to explore alternative battery chemistries such as those based on Aluminum (Al). One of the key challenges associated with the development of Al-ion batteries is the limited choice of cathode materials. In this work, we explore an open-tunnel framework-based oxide (Mo3VOx) as a cathode in an Al-ion battery. The orthorhombic phase of molybdenum vanadium oxide (o-MVO) has been tested previously in Al-ion batteries but has shown poor coulombic efficiency and rapid capacity fade. Our results for o-MVO are consistent with the literature. However, when we explored the trigonal polymorph of MVO (t-MVO), we observe stable cycling performance with much improved coulombic efficiency. At a charge–discharge rate of ~0.4C, a specific capacity of ~190 mAh g−1 was obtained, and at a higher rate of 1C, a specific capacity of ~116 mAh g−1 was achieved. We show that differences in synthesis conditions of t-MVO and o-MVO result in significantly higher residual moisture in o-MVO, which can explain its poor reversibility and coulombic efficiency due to undesirable water interactions with the ionic liquid electrolyte. We also highlight the working mechanism of MVO || AlCl3–[BMIm]Cl || Al to be different than reported previously. 

Looming concerns regarding scarcity, high prices, and safety threaten the long-term use of lithium in energy storage devices. Calcium has been explored in batteries because of its abundance and low cost, but the larger size and higher charge density of calcium ions relative to lithium impairs diffusion kinetics and cyclic stability. In this work, an aqueous calcium–ion battery is demonstrated using orthorhombic, trigonal, and tetragonal polymorphs of molybdenum vanadium oxide (MoVO) as a host for calcium ions. Orthorhombic and trigonal MoVOs outperform the tetragonal structure because large hexagonal and heptagonal tunnels are ubiquitous in such crystals, providing facile pathways for calcium–ion diffusion. For trigonal MoVO, a specific capacity of ∼203 mAh g −1 was obtained at 0.2C and at a 100 times faster rate of 20C, an ∼60 mAh g −1 capacity was achieved. The open-tunnel trigonal and orthorhombic polymorphs also promoted cyclic stability and reversibility. A review of the literature indicates that MoVO provides one of the best performances reported to date for the storage of calcium ions.