Facilitated Phase Transformation for Improved Performance of Earth‐Abundant Disordered Rocksalt Materials
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36 MATERIALS SCIENCE↗
Engineering topics
Ceder, Gerbrand
Publications and source records attributed to Ceder, Gerbrand.
Accelerated data-driven materials science with the Materials Project
The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.
Horton, Matthew K↗
Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework
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.
Chemical reactions↗
Tuning Cation (Dis)Order in Cr‐Based Li‐Excess Oxide Cathode Materials to Improve Li+ Transport Properties
Abstract Li‐excess disordered rocksalts (DRXs) hold promise as next‐generation cathodes for Li‐ion batteries due to their high capacity and energy density, along with the potential to eliminate the need for Co and Ni. However, due to their low Li + diffusivity, DRXs need to be pulverized into nanoparticles to achieve high performance. Herein, a new strategy for overcoming this limitation is demonstrated, involving the design of as‐synthesized partially disordered oxides with a structure that lies between ordered layered and fully disordered, exhibiting a varying degree of local (dis)order. This unique structure activates new Li + diffusion channels, improving percolation and transport properties. This strategy allows a large content of Li + to be accessed in material with large, micron‐sized particles through highly reversible Cr 3+/6+ and O redox, yielding a first discharge capacity of 286 mAh g −1 (881 Wh kg −1 ). The Li + percolation network is further improved by substituting Ti with a mixture of multiple metals, which appears to locally decrease the migration barrier through lattice distortion. Proper tuning of the chemical composition, especially the content of metals with empty d orbitals, is established as a crucial factor for controlling the degree of disorder and mitigating voltage fade and hysteresis growth upon cycling.
Moździerz, Maciej↗
Origin of enhanced performance when Mn-rich rocksalt cathodes transform to δ -DRX
Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformation of Ti-containing Mn-rich disordered rock-salt cathodes into a phase — named δ — with nanoscale spinel-like domains has been shown to increase energy density, capacity retention, and rate capability. However, the nature of the boundaries between domains and their relationship with composition and electrochemistry are not well understood. In this work, we discuss how the transformation into the multi-domain structure results in eight variants of Spinel domains, which is crucial for explaining the nanoscale domain formation in the δ -phase. We study the energetics of crystallographically unique boundaries and the possibility of Li-percolation across them with a fine-tuned CHGNet machine learning interatomic potential. Energetics of 16 d vacancies reveal a strong affinity to segregate to the boundaries, thereby opening Li-pathways at the boundary to enhance long-range Li-percolation in the δ structure. Defect calculations of the relatively low-mobility Ti show how it can influence the extent of Spinel ordering, domain morphology and size significantly; leading to guidelines for engineering electrochemical performance through changes in composition.
Anand, Shashwat↗
Author Correction: A framework to evaluate machine learning crystal stability predictions
In the version of this article initially published, Figs. 1–3, Table 1 and the Supplementary Information presented more models than were present in the accepted version of the article, and which were not discussed in the text. The Supplementary Information has been revised and the figures and table are now updated in the HTML and PDF versions of the article.
Riebesell, Janosh↗
Mitigating Battery Cell Failure: Role of Ag‐Nanoparticle Fillers in Solid Electrolyte Dendrite Suppression
Abstract The development of solid‐state batteries (SSBs) with lithium Li metal anodes holds significant promise for enhancing the energy density and safety of next‐generation energy storage systems. However, their commercialization is hindered by challenges related to Li dendrite formation, which can lead to short circuits and battery failure. In this study, the role of Ag nanoparticles embedded within solid electrolytes (SE) is investigated in suppressing dendrite propagation. The results demonstrate that Ag nanoparticles effectively mitigate two key failure mechanisms: (1) dendrite growth within porous networks at low current densities and (2) stress intensification‐induced SE fracture at higher current densities (12 mA cm −2 ). Ex situ characterization using focused‐ion beam – scanning electron microscopy (FIB–SEM) and energy dispersive X‐ray spectroscopy (EDS,) reveals that Ag nanoparticles migrate alongside advancing Li dendrites, promoting homogeneous dendrite growth and reducing the likelihood of localized stress concentrations. Additionally, the incorporation of Ag nanoparticles is shown to facilitate a more uniform Li distribution toward the anode side, which can potentially enable the use of higher charging rates in SSBs. This study provides a new perspective on Li dendrite suppression and presents new opportunities for enhancing the performance and safety of SSBs.
Diallo, Mouhamad Said↗
Solvent Determines the Formation Pathway in Sol–Gel Synthesized Disordered Rock Salt Material for Lithium Ion Battery Application
The increasing demand for lithium-ion batteries with high capacity and cycling stability, in combination with the scarcity of cobalt and nickel, has led to significant efforts to develop new cathode materials based on earth-abundant transition metals. Mn- and Ti-based disordered rock salt (DRX) cathodes are promising candidates fulfilling these requirements. However, their large-scale fabrication can be energy- and time-intensive using traditional fabrication methods, e.g., solid-state synthesis. The present study showcases sol-gel synthesis as an alternative method with control over the crystallization pathway through solvent choice. Dimethylformamide (DMF) aids the homogenization of the transition metals during early crystallization stages and formation of Li2TiO3 and LiMn2O4 intermediates before the DRX phase is formed. In contrast, 2-methoxyethanol (2-ME) shows transition metal segregation and formation of an additional transition metal intermediate (Ti2MnO4) while not resulting in phase-pure DRX material after calcination. Coin cells prepared with DMF-material yield higher capacity and cycling stability compared with 2-ME material.
Kodalle, Tim↗
A framework to evaluate machine learning crystal stability predictions
The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.
Riebesell, Janosh↗
Interplay between electron localization, magnetic order, and Jahn-Teller distortion dictates LiMnO2 phase stability
The development of manganese (Mn)-rich cathodes for Li-ion batteries promises to alleviate potential supply chain bottlenecks in battery manufacturing. Fundamental challenges in Mn-rich cathodes arise from phenomena such as structural changes due to cooperative Jahn-Teller (JT) distortions of in octahedral environments, Mn migration, and phase transformations to spinel-like order, all of which affect the electrochemical performance. These physically complex phenomena motivate an re-examination of the Li-Mn-O rock-salt space, with a focus on the thermodynamics of the prototypical, polymorphs. It is found that the generalized gradient approximation (GGA-PBEsol) and meta-GGA ( ) density functionals with empirically fitted on-site Hubbard corrections yield spurious stable phases for , such as predicting a phase with -like order ( ) to be the ground state instead of the orthorhombic (Pmmn) phase, which is the experimentally known ground state. Accounting for antiferromagnetic order in each structure is shown to have a substantial effect on the total energies and resulting phase stability. By using hybrid-GGA (HSE06) and GGA with self-consistent Hubbard parameters (on-site and inter-site ) calculated from linear response theory, the experimentally observed phase stability trends are recovered. The calculated on-site between Mn- states in the experimentally observed orthorhombic, layered, and spinel phases are significantly smaller than in and disordered layered structures, by within GGA. The smaller values of are shown to be correlated with a collinear ordering of JT distortions, in which all orbitals are oriented in the same direction. This cooperative JT effect can lead to greater electron delocalization from Mn along the states due to increased Mn-O covalency, which contributes to the greater electronic stability compared to the phases with noncollinear JT arrangements. The structures with collinear ordering of JT distortions also generate greater vibrational entropy, which helps stabilize these phases at high temperature. These phases are shown to be strongly insulating with large calculated band gaps , which are computed using HSE06 and .
Kam, Ronald L↗
Defect-Mediated Diffusion Pathways in Spodumene Accelerate Lithium Transport
Lithium extraction from naturally occurring α-spodumene is hindered by poor lithium diffusivity, necessitating high-temperature phase transformation to a low-density β polymorph. Although β spodumene exhibits up to 5 orders of magnitude higher lithium-ion diffusivity, both phases have diffusion activation energies between 0.8 and 1 eV, indicating that polymorph density is not the controlling factor over diffusivity. We show that aluminum vacancies facilitate lithium-ion diffusion in α-spodumene by reducing the migration barrier from 2.4 to 0.9 eV. Bond valence site energy and nudged elastic band calculations show a new lithium local minimum site which promotes a one-dimensional percolation network by reducing the lithium intersite distance from 4.5 Å to 2.9 Å. However, aluminum vacancies are energetically unfavorable to percolate through the whole structure, resulting in very low net lithium diffusivity and highlighting the critical role of nonstoichiometric defects in facilitating lithium transport in rigid aluminosilicate structures.
Chemical structure↗
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.
chemistry↗
Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation
Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.
Rettenberger, Luca↗
Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model
Solid-state synthesis is widely used to obtain various inorganic materials, such as battery materials and bulk thermoelectrics. Despite its prevalence, the process remains challenging due to the lack of a general theory and well-understood underlying reaction mechanisms. While prior works have successfully extracted structured datasets from literature, they often neglect product phase purity or yield. In this work, we construct a solid-state synthesis dataset consisting of 80,806 syntheses extracted with a large language model (LLM), including 18,869 reactions with impurity phase(s). Our dataset not only validates expected thermodynamic trends for impurity phase formation but also identifies challenging cases where impurity phases emerge even when the target phase is significantly more stable.
Lee, Sanghoon↗
Degradation Mechanism of Phosphate‐Based Li‐NASICON Conductors in Alkaline Environment
NASICON-type Li conductors (Li-NASICON) have traditionally been regarded as promising candidates for solid-state Li-air battery applications because of their stability in water and ambient air. However, the presence of water in the cathode of a Li-air battery can induce a highly alkaline environment by modifying the discharge product from Li 2 O 2 to LiOH which can potentially degrade cathode and separator materials. This study investigates the alkaline stability of common Li-NASICON chemistries through a systematic experimental study of LiTi x Ge 2-x (PO 4 ) 3 (LTGP) with varying x = 0–2.0. Density functional theory calculations are combined to gain a mechanistic understanding of the alkaline instability. It is demonstrated that the instability of LTGP in an alkaline environment is mainly driven by the dissolution of PO 4 3– groups, which subsequently precipitate as Li 3 PO 4 . The introduction of Ti facilitates the formation of a Ti-rich compound on the surface that eventually passivates the material, but only after significant bulk degradation. Consequently, phosphate-based Li-NASICON materials exhibit limited alkaline stability, raising concerns about their viability in humid Li-air batteries.
25 ENERGY STORAGE↗
AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories
AlabOS is a workflow orchestration framework designed to address the increased complexity in autonomous laboratories, featuring a reconfigurable experiment workflow model and a resource reservation mechanism.
Fei, Yuxing↗
Earth-abundant Li-ion cathode materials with nanoengineered microstructures
Manganese-based materials have tremendous potential to become the next-generation lithium-ion cathode as they are Earth abundant, low cost and stable. Here we show how the mobility of manganese cations can be used to obtain a unique nanosized microstructure in large-particle-sized cathode materials with enhanced electrochemical properties. By combining atomic-resolution scanning transmission electron microscopy, four-dimensional scanning electron nanodiffraction and in situ X-ray diffraction, we show that when a partially delithiated, high-manganese-content, disordered rocksalt cathode is slightly heated, it forms a nanomosaic of partially ordered spinel domains of 3–7 nm in size, which impinge on each other at antiphase boundaries. The short coherence length of these domains removes the detrimental two-phase lithiation reaction present near 3 V in a regular spinel and turns it into a solid solution. This nanodomain structure enables good rate performance and delivers 200 mAh g –1 discharge capacity in a (partially) disordered material with an average primary particle size of ~5 µm. The work not only expands the synthesis strategies available for developing high-performance Earth-abundant manganese-based cathodes but also offers structural insights into the ability to nanoengineer spinel-like phases.
36 MATERIALS SCIENCE↗