Publications

2026

1. Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

   Luca Binci, KyuJung Jun, Bowen Deng, and Gerbrand Ceder

   arXiv, 2026

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   Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity (\>\1 mS cm\^{-1}\), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid-state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ an accurately fine-tuned machine learning interatomic potential to construct large-scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal-anion tetrahedra of various lenght. By analyzing the residence time of alkali cations migrating around tetrahedrally-coordinated trivalent metal ions, we find that oxygen anions on the metal-anion tetrahedra limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct-particles correlations, while at the same time they are characterized by an alkali Haven ratio close to one, implying that ionic transport is largely dictated by uncorrelated self-diffusion. Finally, by extending this analysis to chemical compositions \AMX_{2.5}\textbackslashtextsf{O}_{0.75} spanning different alkaline (\A = Li, Na, K), metallic (\M = Al, Ga, In), and halogen (\X = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic chemistry.

2. Discovery of Polymer Electrolytes with Bayesian Optimization and High-Throughput Molecular Dynamics simulations

   Antonia S Kuhn, Jurğis Ruža, KyuJung Jun, Pablo Leon, and Rafael Gómez-Bombarelli

   arXiv, 2026

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   Polymer electrolytes are critical for safe, high-energy-density solid-state batteries, yet discovering candidates that balance high ionic conductivity with high transference numbers remains a significant challenge. In this work, we develop a high-throughput screening platform that utilizes molecular dynamics simulations to navigate a chemical space of 1.7 million hypothetical polymer electrolyte candidates. Data from previous literature is used to warm-start batch Bayesian optimization for iteratively selecting new polymer electrolytes to evaluate. We iteratively identified, evaluated and analyzed 767 homopolymers as potential candidates. Our results reveal several candidates with transport properties exceeding the benchmark polyethylene oxide (PEO)/LiTFSI system. Crucially, our optimization campaigns for ionic conductivity and Li-diffusivity demonstrate that branched architectures and ketone functional groups significantly enhance ion-hopping mechanisms within the polymer matrix. We provide an in-depth mechanistic comparison of Li vs. Na cation transport and offer our open-source framework to accelerate the discovery of liquid, gel, and multi-cation electrolyte systems.

3. Universal Framework for Decomposing Ionic Transport into Interpretable Mechanisms

   KyuJung Jun, Pablo A Leon, Jurğis Ruža, Juno Nam, and Rafael Gómez-Bombarelli

   arXiv, 2026

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   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.

4. MLIP-DFT combined high-throughput screening of alkaline stable oxide Li-ion conductors

   Zhuohan Li^†, KyuJung Jun^†, Bowen Deng, and Gerbrand Ceder

   just accepted at Cell Press Blue, 2026

   

2025

1. The free energy landscape of Li- and Na-ion transport in nanoconfinement with machine learning interatomic potentials

   Sauradeep Majumdar, Swagata Roy, KyuJung Jun, Miguel Steiner, and Rafael Gómez-Bombarelli

   2025

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2. Mechanistic Decomposition of Ion Transport in Amorphous Polymer Electrolytes via Molecular Dynamics

   Pablo A Leon^†, KyuJung Jun^†, Kiarash Gordiz, Yang Shao-Horn, and Rafael Gomez-Bombarelli

   The Journal of Physical Chemistry Letters, 2025

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   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.

3. Ultrafast lithium-ion diffusion in van der Waals superionic conductors

   KyuJung Jun, Sunny Gupta, Bowen Deng, and Gerbrand Ceder

   in preparation, 2025
4. Computational design of polaronic conductive Li-NASICON mixed ionic–electronic conductors

   Jiawei Lin, KyuJung Jun^*, and Gerbrand Ceder^*

   Journal of Materials Chemistry A, 2025

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   The polaron-conductive Li-NASICONs featuring three-dimensional corner-sharing frameworks are potential candidates for mixed ionic electronic conductors in electrochemical energy storage devices.

5. Screening and Development of Sacrificial Cathode Additives for Lithium‐Ion Batteries

   Haegyeom Kim^†, KyuJung Jun^†, Nathan Szymanski, Venkata Sai Avvaru, Zijian Cai, Matthew Crafton, Gi‐Hyeok Lee, Stephen E. Trask, Finn Babbe, Young‐Woon Byeon, Peichen Zhong, Donghun Lee, Byungchun Park, Wangmo Jung, Bryan D. McCloskey, and Wanli Yang

   Advanced Energy Materials, 2025

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   This work presents a computational screening approach to identify Li‐rich transition‐metal oxide sacrificial cathode additives and provides experimental validation of antifluorite‐structured Li6MnO4 as a potential candidate. Initial attempts to synthesize this compound result in low purity (≤40% by weight) owing to close thermodynamic competition with Li2O and MnO at low temperature. However, it is shown that a much higher purity of 85% by weight can be achieved by combining Li excess with rapid cooling from high temperature, which effectively stabilizes the Li6MnO4 phase. The synthesized product delivers a high irreversible Li release capacity that exceeds 700 mAh g−1 by utilizing combined Mn oxidation (Mn2+/3+ and Mn3+/4+) and O oxidation. These results demonstrate that Li6MnO4 may therefore be useful as a potential sacrificial cathode additive in Li‐ion batteries and motivate further investigation of other structurally‐related compounds. While attempts were made to synthesize two additional compounds among computationally screened candidates, it was not successful to experimentally realize the two candidates. The difficulty of experimental realization of the newly predicted materials remains a challenge and it is suggested that more efforts need to be devoted to developing computational techniques to precisely predict synthesizability and propose potential synthetic routes of the predicted materials. This work presents a computational screening approach to identify Li‐rich transition‐metal oxide sacrificial cathode additives and provides experimental validation of antifluorite‐structured Li6MnO4 as a potential candidate.

6. Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

   Lauren N. Walters, Yuxing Fei, Bernardus Rendy, Xiaochen Yang, Mouhamad Diallo, KyuJung Jun, Grace Wei, Matthew J. McDermott, Andrea Giunto, Tara Mishra, Fengyu Shen, David Milsted, May Sabai Oo, Haegyeom Kim, Michael C. Tucker, and Gerbrand Ceder

   Chemistry of Materials, 2025

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   Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

7. Exploring the soft cradle effect and ionic transport mechanisms in the LiMXCl4 superionic conductor family

   KyuJung Jun, Grace Wei, Xiaochen Yang, Yu Chen, and Gerbrand Ceder

   Matter, 2025

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   LiMXCl4, a recently discovered lithium superionic conductor, achieves Li conductivity up to 12.4 mS/cm at room temperature. Notably, LiNbOCl4 features flexible, rotating polyhedra, potentially explaining its high ionic conductivity. However, the generalizability of these findings across different chemistries and the direct link between polyhedra rotations and Li-ion mobility remain unclear. In this study, we explore various M-cation and X-anion substitutions in the LiMXCl4 system, identifying fluoro-chlorides as promising for enhancing electrochemical stability while maintaining high ionic conductivity. Meyer-Neldel analysis on ab initio simulations reveals that LiMXCl4 outperforms existing halide conductors, with projected conductivities of 10–100 mS/cm. Our probabilistic analysis of lithium-ion hops and small-angle tilting events reveals a “soft cradle effect,” where weakly bound M-octahedra tilt in conjunction with Li-ion hops, optimizing the energy landscape. This work provides fundamental insights into the factors driving high ionic conductivity in non-close-packed oxyhalide systems and suggests exciting directions for further improving these materials.

8. Benchmarking Classical Molecular Dynamics Simulations for Computational Screening of Lithium Polymer Electrolytes

   Jurğis Ruža, Pablo Leon, KyuJung Jun, Jeremiah Johnson, Yang Shao-Horn, and Rafael Gómez-Bombarelli

   Macromolecules, 2025

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   Polymer electrolytes may play a crucial role in the development of safe, efficient, and energy-dense batteries thanks to their unique ability to facilitate ion transport while maintaining structural stability. However, experimental discovery is limited by the complexity of synthesizing and testing new monomer and polymer chemistries. In this study, we benchmark the ability of molecular dynamics (MD) simulations with Class 1 force fields to model the transport and structural properties of polymer electrolytes in a high-throughput screening setting. By systematically comparing simulation results to experimental data for 19 polymers, we evaluate the effect of simulation choices in predicting key transport properties. In particular, we evaluate the convergence of diffusivities and conductivities as a function of simulation length and how inaccuracies in modeling polymer glass-transition temperature carry over to ion transport properties. The results highlight both the strengths and limitations of affordable high-throughput MD simulations for these complex systems, providing insights into the optimization of MD simulations for polymer electrolyte research and recommendations for modeling choices with optimal cost-accuracy trade-offs. Furthermore, we perform in-depth transport and structural property analysis across the polymer space to gain insights into the design of new polymer electrolytes.

9. Reply to Smith and Siegel: Most lithium hops in paddlewheel-claimed conductors occur without spatially and temporally correlated anion-group rotations

   KyuJung Jun, and Gerbrand Ceder

   Proceedings of the National Academy of Sciences, 2025

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10. Flow matching for accelerated simulation of atomic transport in crystalline materials

    Juno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, and Rafael Gómez-Bombarelli

    Nature Machine Intelligence, 2025

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    Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular, modelling ionic diffusion in solid-state electrolytes requires methods that can overcome the scale limitations of traditional ab initio molecular dynamics. We introduce LiFlow, a generative framework to accelerate MD simulations for crystalline materials that formulates the task as the conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell–Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7–0.8 for lithium mean squared displacement predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations and maintains high accuracy. With speed-ups of up to 600,000× compared with first-principles methods, LiFlow enables scalable simulations at significantly larger length scales and timescales.

11. Systematic softening in universal machine learning interatomic potentials

    Bowen Deng, Yunyeong Choi, Peichen Zhong, Janosh Riebesell, Shashwat Anand, Zhuohan Li, KyuJung Jun, Kristin A. Persson, and Gerbrand Ceder

    npj Computational Materials, 2025

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    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.

2024

1. Nitride Lithium-ion Conductors with Enhanced Oxidative Stability

   KyuJung Jun, Yihan Xiao, Wenhao Sun, Young-Woon Byeon, Haegyeom Kim, and Gerbrand Ceder

   Journal of The Electrochemical Society, 2024

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   It is desirable to develop solid electrolytes that have both excellent reductive stability against lithium metal and oxidative stability against high-voltage cathodes. However, no inorganic superionic conductors reported thus far satisfy these criteria. Nitrides exhibit intrinsically superior stability against reduction but are often readily oxidized at voltages as low as 0.6 V. In this article, we investigated all nitride-based compounds to search for materials with improved oxidative stabilities over 2.0 V while retaining their intrinsic stability against Li metal. We found two compounds, LiPN2 and Li2CN2, with high oxidative stability > 2.0 V and low vacancy migration energies. Using fine-tuned CHGNet machine-learning interatomic potential, we found that upon introducing aliovalent dopants to introduce vacancies in Li2CN2, the dopant and vacancy strongly anchor with each other to result in trapped vacancies, which lowers ionic conductivity. In contrast, vacancies and dopants have minimal interactions in LiPN2, resulting in a high ionic conductivity. These two compounds were synthesized, but their ionic conductivities were not successfully measured because of the challenges in densification. With improved processing conditions, these compounds may serve as anode-side separators in dual-separator-type all-solid-state batteries or anode buffer layer materials interfaced with lithium metal.

2. Diffusion mechanisms of fast lithium-ion conductors

   KyuJung Jun, Yu Chen, Grace Wei, Xiaochen Yang, and Gerbrand Ceder

   Nature Reviews Materials, 2024

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   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.

3. The nonexistence of a paddlewheel effect in superionic conductors

   KyuJung Jun, Byungju Lee, Ronald L. Kam, and Gerbrand Ceder

   Proceedings of the National Academy of Sciences, 2024

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   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.

4. Effect of Cation Disorder on Lithium Transport in Halide Superionic Conductors

   Peichen Zhong, Sunny Gupta, Bowen Deng, KyuJung Jun, and Gerbrand Ceder

   ACS Energy Letters, 2024

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   Li2ZrCl6 (LZC) is a promising solid-state electrolyte due to its affordability, moisture stability, and high ionic conductivity. We computationally investigate the role of cation disorder in LZC and its effect on Li-ion transport by integrating thermodynamic and kinetic modeling. The results demonstrate that fast Li-ion conductivity requires Li-vacancy disorder, which is dependent on the degree of Zr disorder. The high temperature required to form equilibrium Zr disorder precludes any equilibrium synthesis processes for achieving fast Li-ion conductivity, rationalizing why only nonequilibrium synthesis methods, such as ball-milling, lead to good conductivity. Our simulations show that Zr disorder lowers the Li/vacancy order–disorder transition temperature, which is necessary for creating high Li diffusivity at room temperature. These insights raise a challenge for the large-scale production of these materials and the potential for long-term stability of their properties.

5. Selective formation of metastable polymorphs in solid-state synthesis

   Yan Zeng, Nathan J. Szymanski, Tanjin He, KyuJung Jun, Leighanne C. Gallington, Haoyan Huo, Christopher J. Bartel, Bin Ouyang, and Gerbrand Ceder

   Science Advances, 2024

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   Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO4, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation.

6. Reports From the Frontier: Understanding Complex Correlations in Lithium Superionic Conductors

   KyuJung Jun, and Scott Cushing

   The Electrochemical Society Interface, 2024

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   This feature is intended to let ECS award-winning students and post-docs write primary author perspectives on their field, their work, and where they believe things are going. This month we highlight the work of KyuJung Jun, the 2023 Battery Division Student Award Winner.

7. Revealing Dynamic Evolution of the Anode‐Electrolyte Interphase in All‐Solid‐State Batteries with Excellent Cyclability

   Se Young Kim, Seong‐Min Bak, KyuJung Jun, Gerbrand Ceder, and Guoying Chen

   Advanced Energy Materials, 2024

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   All‐solid‐state‐batteries (ASSBs) based on a halide solid electrolyte (SE) and a lithium‐metal based anode typically have poor cyclability without a buffer layer (such as Li3PS4 or Li6PS5Cl) to prevent the degradation reactions. Here excellent cycling stability of ASSB consisting of an uncoated single‐crystal LiNi0.8Co0.1Mn0.1O2 cathode and a Li3YCl6 (LYC) SE separator in direct contact with a Li‐In anode are demonstrated. Through a combination of electrochemical measurements, synchrotron micro‐X‐ray absorption and diffraction analyses, and density functional theory calculations, reveal for the first time that along with the standard Li+ transport during charge/discharge, indium in the Li‐In anode also participates in the redox reactions. The in‐situ generated In3+ preferentially occupies the vacant Li sites in the trigonal LYC lattice, leading to the formation, growth, and eventual stabilization of an anode‐electrolyte interphase (AEI) layer consisting of an In‐doped Li3‐xInxYCl6 (x = ≈0.2) phase. It is discussed how the presence of such an AEI layer prevents LYC decomposition and suppresses dendrite formation and propagation, enabling stable cycling of ASSB with ≈90% capacity retention over 1000 cycles. This work sheds light on the dynamic evolution of the halide SE and alloy anode interphase, and opens new avenues in the future design of long‐lasting high‐energy all‐solid‐state‐batteries. This study reveals In redox reactions at the interface between a halide solid‐electrolyte and a Li‐In alloy anode. The migration of the in situ generated In3+ leads to the formation, growth, and eventual stabilization of a Li3‐xInxYCl6 anode‐electrolyte interphase over cycling. Such dynamic evolution enables the excellent performance of high‐energy NMC811 all‐solid‐state‐battery cells, with ≈90% capacity retention after 1000 cycles.

8. Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis

   Zheren Wang, Yingzhi Sun, Kevin Cruse, Yan Zeng, Yuxing Fei, Zexuan Liu, Junyi Shangguan, Young-Woon Byeon, KyuJung Jun, Tanjin He, Wenhao Sun, and Gerbrand Ceder

   Nature Synthesis, 2024

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   Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO3)4 and LiFePO4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized. Precipitation of target functional materials from water is sensitive to precursor selection and aqueous electrochemistry (pH and redox potential), where competition between thermodynamics and kinetics can yield undesired impurity phases. Now, a theoretical framework to identify optimal synthesis conditions of target materials is developed and validated against a literature dataset and direct experiments.

2023

1. Understanding the Irreversible Reaction Pathway of the Sacrificial Cathode Additive Li6CoO4

   KyuJung Jun, Lori Kaufman, Wangmo Jung, Byungchun Park, Chiho Jo, Taegu Yoo, Donghun Lee, Byungju Lee, Bryan D. McCloskey, Haegyeom Kim, and Gerbrand Ceder

   Advanced Energy Materials, 2023

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   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.

2. Unraveling Li growth kinetics in solid electrolytes due to electron beam charging

   Xinxing Peng, Qingsong Tu, Yaqian Zhang, KyuJung Jun, Fengyu Shen, Tofunmi Ogunfunmi, Yingzhi Sun, Michael C. Tucker, Gerbrand Ceder, and Mary C. Scott

   Science Advances, 2023

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   Revealing the local structure of solid electrolytes (SEs) with electron microscopy is critical for the fundamental understanding of the performance of solid-state batteries (SSBs). However, the intrinsic structural information in the SSB can be misleading if the sample’s interactions with the electron beams are not fully understood. In this work, we systematically investigate the effect of electron beams on Al-doped lithium lanthanum zirconium oxide (LLZO) under different imaging conditions. Li metal is observed to grow directly on the clean surface of LLZO. The Li metal growth kinetics and the morphology obtained are found to be heavily influenced by the temperature, accelerating voltage, and electron beam intensity. We prove that the lithium growth is due to the LLZO delithiation activated by a positive charging effect under electron beam emission. Our results deepen the understanding of the electron beam impact on SEs and provide guidance for battery material characterization using electron microscopy.

3. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

   Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J. Bartel, and Gerbrand Ceder

   Nature Machine Intelligence, 2023

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   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

4. Crystal Structures and Phase Stability of the Li2S–P2S5 System from First Principles

   Ronald L. Kam, KyuJung Jun, Luis Barroso-Luque, Julia H. Yang, Fengyu Xie, and Gerbrand Ceder

   Chemistry of Materials, 2023

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   The Li2S–P2S5 pseudo-binary system has been a valuable source of promising superionic conductors, with α-Li3PS4, β-Li3PS4, HT-Li7PS6, and Li7P3S11 having excellent room-temperature Li-ion conductivity >0.1 mS/cm. The metastability of these phases at ambient temperature motivates a study to quantify their thermodynamic accessibility. Through calculating the electronic, configurational, and vibrational sources of free energy from first principles, a phase diagram of the crystalline Li2S–P2S5 space is constructed. New ground-state orderings are proposed for α-Li3PS4, HT-Li7PS6, LT-Li7PS6, and Li7P3S11. Well-established phase stability trends from experiments are recovered, such as polymorphic phase transitions in Li7PS6 and Li3PS4, and the instability of Li7P3S11 at high temperature. At ambient temperature, it is predicted that all superionic conductors in this space are indeed metastable but thermodynamically accessible. Vibrational and configurational sources of entropy are shown to be essential toward describing the stability of superionic conductors. New details of the Li sublattices are revealed and are found to be crucial toward accurately predicting configurational entropy. All superionic conductors contain significant configurational entropy, which suggests an inherent correlation between fast Li diffusion and thermodynamic stability arising from the configurational disorder.

5. Unraveling Li Growth Kinetics in Solid Electrolytes Due to Charging Effect under Electron Microscopy

   Tofunmi Ogunfunmi, Xinxing Peng, Qingsong Tu, Yaqian Zhang, KyuJung Jun, Fengyu Shen, Yingzhi Sun, Michael C Tucker, Gerbrand Ceder, and M C Scott

   Microscopy and Microanalysis, 2023

   DOI

   
6. Weak Correlation between the Polyanion Environment and Ionic Conductivity in Amorphous Li–P–S Superionic Conductors

   Byungju Lee, KyuJung Jun, Bin Ouyang, and Gerbrand Ceder

   Chemistry of Materials, 2023

   DOI

   

2022

1. Lithium superionic conductors with corner-sharing frameworks

   KyuJung Jun, Yingzhi Sun, Yihan Xiao, Yan Zeng, Ryounghee Kim, Haegyeom Kim, Lincoln J. Miara, Dongmin Im, Yan Wang, and Gerbrand Ceder

   Nature Materials, 2022

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   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.

2. Understanding of electrochemical K+/Na+ exchange mechanisms in layered oxides

   Haegyeom Kim, Young-Woon Byeon, Jingyang Wang, Yaqian Zhang, Mary C. Scott, KyuJung Jun, Zijian Cai, and Yingzhi Sun

   Energy Storage Materials, 2022

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   Ion-exchange reactions are commonly used to develop novel metastable electrode materials for alkali-ion batteries that cannot be synthesized using direct chemical reactions. In this study, the electrochemical K to Na ion-exchange reaction mechanisms in a layered K x CoO2 cathode as a model system were investigated using operando and ex situ structure characterization techniques. Some level of K ions was observed to remain in the layered structure during the electrochemical ion-exchange reactions. Interestingly, the K ions are well separated from the Na-rich phases in the discharged state, and they form an intermediate phase in which K and Na ions are mixed at the top of charge. We discovered that such residual K ions prevent the collapse of the layered structure in the high-voltage regime, thereby improving the cycling stability in a Na-battery system.

2021

1. Lithium Oxide Superionic Conductors Inspired by Garnet and NASICON Structures

   Yihan Xiao^†, KyuJung Jun^†, Yan Wang, Lincoln J. Miara, Qingsong Tu, and Gerbrand Ceder

   Advanced Energy Materials, 2021

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   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.

2019

1. A comparative study on modeling of the ferromagnetic and paramagnetic states of uranium hydride using a DFT+ U method

   KyuJung Jun, Jae-Uk Lee, Min Ho Chang, and Takuji Oda

   Physical Chemistry Chemical Physics, 2019

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   Uranium hydride is a promising material for stationary hydrogen storage in fusion reactors. In this work, various material properties of uranium hydride in both ferromagnetic (FM) and paramagnetic (PM) states are calculated to determine the optimal first-principles calculation method. For the treatment of strongly correlated f-electrons, the PBE functional with a Hubbard U parameter of 0.6 eV is selected as the optimal method and provides accurate formation energies and reasonable structural properties of the FM state. Using this method, we test four model spin configurations to approximately simulate the PM state: FM, antiferromagnetic (AFM), special quasi-random structure (SQS) and nonmagnetic (NM) configurations. The FM and AFM configurations provide formation energy and lattice constants comparable to those of the SQS configuration, which is used as the reference PM state. In addition, the experimental results on thermal expansion and the bulk modulus in the PM states are well reproduced with the FM, AFM and SQS configurations. These results demonstrate that PBE+U with FM, AFM and SQS configurations can approximately simulate the PM states, although there are some properties that can only be qualitatively reproduced by DFT calculations, such as the magnetic transition. This study enables the design of multiscale modeling for uranium hydride while maintaining simultaneous efficiency and accuracy.