Materials Intelligence for Design and Simulations Lab

Understanding mechanisms of ion transport in bulk materials is central to designing next-generation ion conductors for energy storage devices, yet studies employing all-atom molecular dynamics (MD) have largely been limited to reporting overall transport coefficients without a quantitative, spatiotemporally resolved breakdown of \textbackslashemph{how} charge is carried. We present a computational framework that analyzes MD trajectories to quantitatively interpret macroscopic transport by decomposing it into additive contributions from physically motivated events. They are defined either through heuristically identified microscopic transitions, capturing events such as single-ion hops, multi-ion hops, and vehicular motion, or through transitions between chemically interpretable coordination macrostates. The construction guarantees that attributed contributions sum exactly to the Onsager transport coefficients estimated via the Green-Kubo/Einstein formalism, while scanning the sampling window exposes characteristic temporal scales at which distinct transport mechanisms emerge and dominate. Applied across three prototypical electrolytes-inorganic crystals, liquids, and polymers-the framework quantitatively resolves long-standing debates (e.g., the role of concerted motion and exchange), identifies dominant mechanisms and rate-limiting steps, quantifies their frequencies and effectiveness, and extracts activation energies for distinct transport modes, thereby distilling design rules for fast conduction. This general and reproducible analysis tool turns MD trajectories into quantitative mechanism maps, enabling the ion-conductor community to adjudicate mechanistic hypotheses and accelerate discovery.

Understanding ion transport in polymer electrolytes is critical for designing next-generation energy storage systems. Molecular dynamics simulations offer complete atomistic information, but disentangling the contributions of distinct diffusion modes in cation transport remains a challenge. Here, we introduce a mathematical algorithm that decomposes the transport coefficient into a sum of interpretable diffusion mechanisms based on changes in local atomic environment. Applying this framework to a prototypical polymer electrolyte, we quantify the contributions of proposed transport modes. We identify a rare lithium diffusion mechanism associated with the disassembly of existing solvation environments which contributes an order of magnitude more to lithium transport properties per event than all other mechanisms. Finally, we characterize the spectrum of microscopic diffusion events, providing a detailed and quantitative understanding of ion transport in polymer electrolytes. Our approach offers a promising path toward a more quantitative and mechanistic understanding of ion transport in soft matter electrolytes.

The quest for next-generation energy-storage technologies has pivoted towards all-solid-state batteries, primarily owing to their potential for enhanced safety and energy density. At the centre of this promising technology lie inorganic lithium superionic conductors, which facilitate rapid ion transport comparable to that in their liquid counterparts. Despite their promise, the limited availability of materials that both achieve superionic conductivity and fulfil all practical requirements necessitates the discovery of novel conductors. This Review comprehensively explores the diverse structural and chemical factors that improve ionic conductivity and the atomistic mechanism by which each factor affects it. We emphasize the importance of a dual approach: using structural factors to enable high-conducting prototypes, and chemical factors to further optimize the ionic conductivity. From these insights, we distil over 40 years of conductor development history to the key concepts that paved the way for today’s leading superionic conductors. In detailing the trajectory of ionic conduction advancements, this Review not only charts the progress in the field but also proposes a strategic approach for researchers to efficiently innovate with the ultimate goal of realizing the promise of all-solid-state batteries. Inorganic lithium superionic conductors are central to the development of solid-state batteries, but the availability of practical superionic conductors is still limited. This Review highlights structural and chemical strategies to enhance ionic conductivity and maps a strategic approach to discover, design and optimize fast lithium-ion conductors for safe and high-energy-density all-solid-state batteries.

Since the 1980s, the paddlewheel effect has been suggested as a mechanism to boost lithium-ion diffusion in inorganic materials via the rotation of rotor-like anion groups. However, it remains unclear whether the paddlewheel effect, defined as large-angle anion group rotations assisting Li hopping, indeed exists; furthermore, the physical mechanism by which the anion-group dynamics affect lithium-ion diffusion has not yet been established. In this work, we differentiate various types of rotational motions of anion groups and develop quaternion-based algorithms to detect, quantify, and relate them to lithium-ion motion in ab initio molecular dynamics simulations. Our analysis demonstrates that, in fact, the paddlewheel effect, where an anion group makes a large angle rotation to assist a lithium-ion hop, does not exist and thus is not responsible for the fast lithium-ion diffusion in superionic conductors, as historically claimed. Instead, we find that materials with topologically isolated anion groups can enhance lithium-ion diffusivity via a more classic nondynamic soft-cradle mechanism, where the anion groups tilt to provide optimal coordination to a lithium ion throughout the hopping process to lower the migration barrier. This anion-group disorder is static in nature, rather than dynamic and can explain most of the experimental observations. Our work substantiates the nonexistence of the long-debated paddlewheel effect and clarifies any correlation that may exist between anion-group rotations and fast ionic diffusion in inorganic materials.

The use of a sacrificial cathode additive that contains a large amount of lithium is one potential solution to compensate for the irreversible capacity loss associated with next‐generation anodes such as silicon. Antifluorite‐type Li6CoO4 has attracted attention as a potential cathode additive owing to its remarkably high theoretical lithium extraction capacity. However, the complex mechanism of lithium extraction as well as the oxygen loss from Li6CoO4 is not well understood. A generalizable computational thermodynamics and experimental framework is presented to understand the lithium‐extraction pathway of Li6CoO4. It is found that one lithium per formula unit can be topotactically extracted from Li6CoO4, followed by an irreversible and nontopotactic phase transformation to Li2CoO3 or LiCoO2 depending on the temperature. The results show that peroxide species may form to charge‐compensate for Li extraction which is undesirable as this can lead to gas release during battery operation. It is suggested that charging Li6CoO4 at an elevated temperature that the electrolyte can withstand, redirects the reaction pathway and prevents the formation of intermediate peroxide species making it an effective and stable sacrificial cathode additive. Next‐generationsilicon anodes suffer from severe initial capacity loss. Antifluorite‐type Li6CoO4 can serve as an attractive sacrificial cathode additive by compensating for the irreversible capacity loss. A generalizable computational and experimental framework is presented to understand the lithium‐extraction pathway of Li6CoO4. The results provide insights on methods to redirect its reaction pathway to serve as a stable sacrificial cathode additive.

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. An outstanding challenge in materials science is doing large-scale simulations with complex electron interactions. Deng and colleagues introduce a universal graph neural network-based interatomic potential integrating atomic magnetic moments as charge constraints, which allows for capturing subtle chemical properties in several lithium-based solid-state materials

Superionic lithium conductivity has only been discovered in a few classes of materials, mostly found in thiophosphates and rarely in oxides. Herein, we reveal that corner-sharing connectivity of the oxide crystal structure framework promotes superionic conductivity, which we rationalize from the distorted lithium environment and reduced interaction between lithium and non-lithium cations. By performing a high-throughput search for materials with this feature, we discover ten new oxide frameworks predicted to exhibit superionic conductivity—from which we experimentally demonstrate LiGa(SeO3)2 with a bulk ionic conductivity of 0.11 mS cm−1 and an activation energy of 0.17 eV. Our findings provide insight into the factors that govern fast lithium mobility in oxide materials and will accelerate the development of new oxide electrolytes for all-solid-state batteries. Superionic lithium conductivity has only been observed in a few classes of materials, mostly in thiophosphates but rarely in oxides. Corner-sharing connectivity in an oxide crystal structure framework is now shown to promote superionic conductivity.

The key component in lithium solid‐state batteries (SSBs) is the solid electrolyte composed of lithium superionic conductors (SICs). Lithium oxide SICs offer improved electrochemical and chemical stability compared with sulfides, and their recent advancements have largely been achieved using materials in the garnet‐ and NASICON (sodium superionic conductor)‐ structured families. In this work, using the ion‐conduction mechanisms in garnet and NASICON as inspiration, a common pattern of an “activated diffusion network” and three structural features that are beneficial for superionic conduction: a 3D percolation Li diffusion network, short distances between occupied Li sites, and the “homogeneity” of the transport path are identified. A high‐throughput computational screening is performed to search for new lithium oxide SICs that share these features. From this search, seven candidates are proposed exhibiting high room‐temperature ionic conductivity evaluated using ab initio molecular dynamics simulations. Their structural frameworks including spinel, oxy‐argyrodite, sodalite, and LiM(SeO3)2 present new opportunities for enriching the structural families of lithium oxide SICs. By representing the diffusion mechanism of garnet and NASICON structures in ion diffusion networks, a common pattern of “activated local environments” is identified. High‐throughput computational screening is performed to search for novel fast Li‐ion conductors that share this feature and seven candidates are proposed that exhibit high room‐temperature ionic conductivity, enriching the currently limited structural families of oxide superionic conductors.