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Disordered rocksalt oxide (DRX) cathodes are promising candidates for next-generation Co- and Ni-free Li-ion batteries. While fluorine substitution for oxygen has been explored as an avenue to enhance their performance, the amount of fluorine incorporated into the DRX structure is particularly challenging to quantify and impedes our ability to relate fluorination to electrochemical performance. Herein, an experimental-computational method combining 7Li and 19F solid-state nuclear magnetic resonance, and ab initio cluster expansion Monte Carlo simulations, is developed to determine the composition of DRX oxyfluorides. Using this method, the synthesis of Mn- and Ti-containing DRX via standard high temperature sintering and microwave heating is optimized. Further, the upper fluorination limit attainable using each of these two synthesis routes is established for various Mn-rich DRX compounds. A comparison of their electrochemical performance reveals that the capacity and capacity retention mostly depend on the Mn content, while fluorination plays a secondary role.

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. We present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in LixMnO2, the finite temperature phase diagram for LixFePO4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

Li-rich rocksalt oxides are promising cathode materials for lithium-ion batteries due to their large capacity and energy density, and their ability to use earth-abundant elements. The excess Li in the rocksalt, needed to achieve good Li transport, reduces the theoretical transition metal redox capacity and introduces a labile oxygen state, both of which lead to increased oxygen oxidation and concomitant capacity loss with cycling. Herein, it is demonstrated that substituting the labile oxygen in Li-rich cation-disordered rocksalt materials with a vacancy is an effective strategy to inhibit oxygen oxidation. It is found that the oxygen vacancy in cation-disordered lithium manganese oxide favors high Li coordination thereby reducing the concentration of unhybridized oxygen states, while increasing the theoretical Mn capacity. It is shown that in the vacancy-containing compound, synthesized by ball milling, the Mn valence is lowered to less than +3, providing access to more than 300 mAh g−1 capacity from the Mn2+/Mn4+ redox reservoir. The increased transition metal redox and decreased O oxidation are found to improve the capacity and voltage retention, indicating that oxygen vacancy creation to remove the most vulnerable oxygen ions and reduce transition metal valence provides a new opportunity for the design of high-performance Li-rich rocksalt cathodes.

The cluster expansion (CE) method has seen continuous and increasing use in the study of configuration-dependent properties of crystalline materials. The original development of the CE method along with the underlying mathematical formalism and assumptions was focused on the study of metallic alloys. Since then the methodology has been actively and successfully used in the study of ionic materials as well. In this work, we present a cohesive reformulation of the mathematical formalism underlying the CE method based on a synthesis of its original formulation, several additions and extensions that have been proposed since, and a revised representation of its constituent mathematical objects. We then proceed to describe some of the formal implications of using the methodology for charge-neutral configurations in ionic systems. In particular, we discuss the reduction of the size of configuration spaces and the resulting linear dependencies that arise among correlation functions that span the larger unconstrained configuration space. Additionally, we explore the effects of long-range electrostatic interactions. We also demonstrate how the previously proposed use of a point electrostatic term successfully accounts for the majority of the longer-range electrostatic interactions, and leaves the cluster expansion terms to capture mostly short-range interactions. Finally, we present and discuss a variety of recently developed methodologies, including training structure selection, oxidation state assignment, structure mapping, and regression algorithms, that are necessary to address these formal mathematical notions for a practical implementation of the CE method in the study of multicomponent ionic materials.

We introduce ℓ0ℓ2-norm regularization and hierarchy constraints into linear regression for the construction of cluster expansions to describe configurational disorder in materials. The approach is implemented through mixed integer quadratic programming (MIQP). The ℓ2-norm regularization is used to suppress intrinsic data noise, while the ℓ0-norm is used to penalize the number of nonzero elements in the solution. The hierarchy relation between clusters imposes relevant physics and is naturally included by the MIQP paradigm. As such, sparseness and cluster hierarchy can be well optimized to obtain a robust, converged set of effective cluster interactions with improved physical meaning. We demonstrate the effectiveness of ℓ0ℓ2-norm regularization in two high-component disordered rocksalt cathode material systems, where we compare the cross-validation, convergence speed, and the reproduction of phase diagrams, voltage profiles, and Li-occupancy energies with those of the conventional ℓ1-norm regularized cluster expansion models.

Rechargeable batteries based on multivalent working ions are promising candidates for next-generation high-energy-density batteries. Development of these technologies, however, is largely limited by the low diffusion rate of multivalent ions in solid-state materials, thereby necessitating a better understanding of the design principles that control multivalent-ion mobility. Here, we report Ca1.5Ba0.5Si5O3N6 as a potential calcium solid-state conductor and investigate its Ca migration mechanism by means of ab initio computations and neutron diffraction. This compound contains partially occupied Ca sites in close proximity to each other, providing a unique mechanism for Ca migration. Nuclear density maps obtained with the maximum entropy method from neutron powder diffraction data provide strong evidence for low-energy percolating one-dimensional pathways for Ca-ion migration. Ab initio molecular dynamics simulations further support a low Ca-ion migration barrier of ∼400 meV when Ca vacancies are present and reveal a unique "vacancy-adjacent"concerted ion migration mechanism. This work provides a new understanding of solid-state Ca-ion diffusion and insights into the future design of novel cation configurations that utilize the interactions between mobile ions to enable fast multivalent-ion conduction in solid-state materials.

High-rate cathode materials for Li-ion batteries require fast Li transport kinetics, which typically rely on topotactic Li intercalation/de-intercalation because it minimally disrupts Li transport pathways. In contrast to this conventional view, here we demonstrate that the rate capability in a Li-rich cation-disordered rocksalt cathode can be significantly improved when the topotactic reaction is replaced by a non-topotactic reaction. The fast non-topotactic lithiation reaction is enabled by facile and reversible transition metal octahedral-to-tetrahedral migration, which improves rather than impedes Li transport. Using this concept, we show that high-rate performance can be achieved in Mn- and Ni-based cation-disordered rocksalt materials when some of the transition metal content can reversibly switch between octahedral and tetrahedral sites. This study provides a new perspective on the design of high-performance cathode materials by demonstrating how the interplay between Li and transition metal migration in materials can be conducive to fast non-topotactic Li intercalation/de-intercalations.

Using both computations and experiments, we demonstrate that the performance of Li-excess cation-disordered rocksalt cathodes can be improved by Mg substitution. Mg reduces the amount of Li in the compound that is strongly bound to F and thereby increases the capacity. This enables the use of fluorination as a tool to improve stability of the compounds without significant loss of capacity. Mg emerged as the most optimal substitution element from a systematic computational study aimed at identifying inactive doping elements with a strong enough bonding strength to fluorine to displace Li from the F environments. Our results also show that capacity can be traded for cycle life depending on whether Mg is substituted for Li or for the redox metal. This design strategy should be considered in fluorinated cathodes, which will facilitate the design of optimized disordered rocksalt oxyfluoride cathodes.

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs. [ABSTRACT FROM AUTHOR]

Li‐excess disordered rocksalts (DRXs) are emerging as promising cathode materials for Li‐ion batteries due to their ability to use earth‐abundant transition metals. In this work, a new strategy based on partial Li deficiency engineering is introduced to optimize the overall electrochemical performance of DRX cathodes. Specifically, by using Mn‐based DRX as a proof‐of‐concept, it is demonstrated that the introduction of cation vacancies during synthesis (e.g., Li1.3‐xMn2+0.4‐xMn3+xNb0.3O1.6F0.4, x = 0, 0.2, and 0.4) improves both the discharge capacity and rate performance due to the more favored short‐range order in the presence of Mn3+. Density functional theory calculations and Monte Carlo simulations, in combination with spectroscopic tools, reveal that introducing 10% vacancies (Li1.1Mn2+0.2Mn3+0.2Nb0.3O1.6F0.4) enables both Mn2+/Mn3+ redox and excellent Li percolation. However, a more aggressive vacancy doping (e.g., 20% vacancies in Li0.9Mn3+0.4Nb0.3O1.6F0.4) impairs performance because it induces phase separation between an Mn‐rich and a Li‐rich phase. [ABSTRACT FROM AUTHOR]