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Ceder, Gerbrand

Publications and source records attributed to Ceder, Gerbrand.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli–electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Alkali‐Ion‐Assisted Activation of ε‐VOPO 4 as a Cathode Material for Mg‐Ion Batteries

Abstract Rechargeable multivalent‐ion batteries are attractive alternatives to Li‐ion batteries to mitigate their issues with metal resources and metal anodes. However, many challenges remain before they can be practically used due to the low solid‐state mobility of multivalent ions. In this study, a promising material identified by high‐throughput computational screening is investigated, ε‐VOPO 4 , as a Mg cathode. The experimental and computational evaluation of ε‐VOPO 4 suggests that it may provide an energy density of >200 Wh kg −1 based on the average voltage of a complete cycle, significantly more than that of well‐known Chevrel compounds. Furthermore, this study finds that Mg‐ion diffusion can be enhanced by co‐intercalation of Li or Na, pointing at interesting correlation dynamics of slow and fast ions.

25 ENERGY STORAGE↗
DOE OSTI2024

Fast Room-Temperature Mg-Ion Conduction in Clay-Like Halide Glassy Electrolytes

The discovery of mechanically soft solid-state materials with fast Mg-ion conduction is crucial for the development of solid-state magnesium batteries. In this paper, novel magnesium gallium halide compounds are reported that achieve high ionic conductivity of 0.47 mS cm -1 at room temperature. These Mg-ion conductors obtained by ball milling Mg and Ga salts exhibit clay-like mechanical properties, enabling intimate contact at the electrode–electrolyte interface during battery cycling. With a combination of experimental and computational analysis, this study identifies that the soft-clay formation is induced by partial anion exchange during milling. This partial anion exchange creates undercoordinated magnesium ions in a chlorine-rich environment, yielding fast Mg-ion conduction. This work demonstrates the potential of clay-like halide electrolytes for all-solid-state magnesium batteries, with possible further extension to other multivalent battery systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The nonexistence of a paddlewheel effect in superionic conductors

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.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electrochemistry in the Large Tunnels of Lithium Postspinel Compounds

Lithium spinels (Li MM 'O 4 ) are an important class of mixed-cation materials that have found uses in batteries, catalysis, and optics. Postspinels are a series of related framework structures with the AMM 'O 4 host composition that are formed with larger A -site cations, typically under high pressure. Postspinels have one-dimensional tunnel structures with pores that are larger than those in spinel and triangular in cross-section, but they are relatively unexplored as intercalation electrodes. While lithium postspinels have been previously found to be thermodynamically stable only at high pressures, we have identified a synthetic pathway that produces the lithium-containing materials at ambient pressure using an ion-exchange process from the corresponding sodium postspinels. Here, in this work, we report the synthesis and a survey of the electrochemical properties of 10 new lithium CaFe 2 O 4 -type postspinel compounds where M = Mn 3+ , V 3+ , Cr 3+ , Rh 3+ , Fe 2+ , Mg 2+ , Co 2+ and M ' = Ti 4+ and/or Sn 4+ . Although complete delithiation is not achieved during electrochemical cycling, many of the lithium postspinels have substantial charge storage capacity in Li battery cells owing to the ability of the large framework tunnels to accommodate more than one lithium ion per formula unit. Multiple redox couples are accessed for LiMnSnO 4 , Li 0.96 Mn 0.96 Sn 1.04-x Ti x O 4 , Li 0.96 V 0.96 Ti 1.04 O 4 , Li 0.96 Cr 0.96 Ti 1.04 O 4 , and LiFe 0.5 Ti 1.5 O 4 . Compositions with moderate or poor lithium cyclability are also discussed for comparison. Redox mechanisms and trends are identified by comparing this new redox-active framework to related spinels, ramsdellites, and 'Na 0.44 MnO 2 ' structures, and from density functional theory (DFT) electronic structures. Operando diffraction shows complex structural responses to lithium insertion and extraction in this postspinel framework. A DFT framework was proposed to identify promising lithium postspinel phases that could be accessed metastably under ambient pressure conditions and to assess their stability to lithium insertion and extraction. This work suggests that CaFe 2 O 4 -type hosts are a promising new class of lithium-ion energy storage materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

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. Here, 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.

25 ENERGY STORAGE↗
DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Unlocking Li superionic conductivity in face-centred cubic oxides via face-sharing configurations

Oxides with a face-centred cubic (fcc) anion sublattice are generally not considered as solid-state electrolytes as the structural framework is thought to be unfavourable for lithium (Li) superionic conduction. Here we demonstrate Li superionic conductivity in fcc-type oxides in which face-sharing Li configurations have been created through cation over-stoichiometry in rocksalt-type lattices via excess Li. We find that the face-sharing Li configurations create a novel spinel with unconventional stoichiometry and raise the energy of Li, thereby promoting fast Li-ion conduction. The over-stoichiometric Li–In–Sn–O compound exhibits a total Li superionic conductivity of 3.38 × 10 -4 S cm -1 at room temperature with a low migration barrier of 255 meV. Our work unlocks the potential of designing Li superionic conductors in a prototypical structural framework with vast chemical flexibility, providing fertile ground for discovering new solid-state electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Microscopic Mechanism of Lithiation and Delithiation in the Ag/C Buffer Layer for Anode‐Free Solid‐State Batteries

Abstract Lithium metal solid‐state batteries (LMSSBs) have demonstrated their high energy density and cycling performance at high current densities in an anode‐free architecture, featuring a thin Ag/C composite buffer layer (BL) between the current collector (CC) and the solid electrolyte (SE). This study explains the microscopic mechanism of the Ag/C BL by using first‐principles atomistic and continuum modeling. It is shown that Ag effectively acts as a homogeneous solid‐solution beyond AgLi 2.32 and maintains a positive potential even at AgLi 25 during lithiation. Key factors underlying the working of the Ag/C BL include lower interfacial resistance at the BL/CC interface than at the BL/SE interface, leading to predominant Li deposition on BL/CC, and substantial Ag–Li volume expansion during lithiation. This, combined with stronger BL/SE adhesion, causes BL/SE separation and Ag–Li extrusion toward the CC side. During delithiation, Ag re‐precipitates as nanoparticles uniformly on the CC, with its positive lithiation potential homogenizing Li currents in subsequent cycles. Other metals are less effective due to their relatively large overpotential, premature lithiation termination, and limited volume expansions hindering movement toward the CC. The study aids the BL design, focusing on metal choice and optimization material and microstructural properties, such as the Li‐ion conductivity and interfacial resistance.

25 ENERGY STORAGE↗
DOE OSTI2024

Selective formation of metastable polymorphs in solid-state synthesis

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 LiTiOPO 4 , 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.

36 MATERIALS SCIENCE↗