arXiv subjects

Gerbrand Ceder

Publications and source records attributed to Gerbrand Ceder.

At least 37 records · Page 2Linked to original sources

arXiv2025

Investigating the Degradation of LATP Solid Electrolyte in High Alkaline Li-$O_2$ Batteries

In this study, we address the challenge of electrolyte degradation in all-solid-state humidified Li-$O_2$ batteries, which offer high theoretical energy density and potential cost advantages over conventional lithium-ion batteries. Combining STEM-EELS, and XPS characterizations with DFT calculations, we reveal the leaching of $(PO_4)^{3+}$ and $Al^{3+}$ ions from the $Li_{1.3}Al_{0.3}Ti_{1.7}(PO_4)_3$ (LATP) solid electrolyte upon battery discharge, caused by the highly alkaline environment. Upon charging, the leached ions precipitate as $Li_3PO_4$ and $AlPO_4$, which accumulate on the LATP surface and contribute to battery degradation. A Ti-rich layer is observed at the surface after a few cycles due to depletion of other cations. Our findings suggest that the degradation products are formed through repeated dissolution and precipitation in the discharge-charge cycles. Furthermore, our results indicate that the Ti-rich layer on the LATP surface can potentially reduce parasitic reactions. Our study provides mechanistic understanding of LATP solid electrolyte degradation in humidified Li-$O_2$ cell, paving the way for designing more durable and efficient Li-$O_2$ batteries.

cond-mat.mtrl-sci↗

arXiv2025

TorchSim: An efficient atomistic simulation engine in PyTorch

We introduce TorchSim, an open-source atomistic simulation engine tailored for the Machine Learned Interatomic Potential (MLIP) era. By rewriting core atomistic simulation primitives in PyTorch, TorchSim can achieve orders of magnitude acceleration for popular MLIPs. Unlike existing molecular dynamics packages, which simulate one system at a time, TorchSim performs batched simulations that efficiently utilize modern GPUs by evolving multiple systems concurrently. TorchSim supports molecular dynamics integrators, structural relaxation optimizers, both machine-learned and classical interatomic potentials (such as Lennard-Jones, Morse, soft-sphere), batching with automatic memory management, differentiable simulation, and integration with popular materials informatics tools.

physics.comp-ph↗

arXiv2025

The Interplay Between Electron Localization, Magnetic Order, and Jahn-Teller Distortion that Dictates LiMnO$_2$ Phase Stability

The development of Mn-rich cathodes for Li-ion batteries promises to alleviate supply chain bottlenecks in battery manufacturing. Challenges in Mn-rich cathodes arise from Jahn-Teller (JT) distortions of Mn$^{3+}$, Mn migration, and phase transformations to spinel-like order, which can affect the electrochemical performance. These phenomena motivate an ab initio re-examination of the thermodynamics of the LiMnO2 polymorphs. It is found that the generalized gradient approximation (GGA - PBEsol) and meta-GGA (r2SCAN) density functionals with empirical on-site Hubbard U corrections yield spurious stable phases for LiMnO2, such as predicting a phase with gamma-LiFeO2-like order (g-LiMnO2) to be the ground state instead of the orthorhombic (Pmmn) phase, which is the experimentally known ground state. Accounting for the antiferromagnetic (AFM) order in each structure has 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 U and inter-site V), the experimentally observed LiMnO2 phase stability trends are recovered. The calculated Hubbard U in the experimentally observed orthorhombic, layered, and spinel phases are significantly smaller than U in g-LiMnO2 and disordered layered structures. The smaller values of U are correlated with a collinear ordering of JT distortions, in which all $e_g$ orbitals are oriented in the same direction. This cooperative JT effect leads to increased Mn-O covalency, which contributes to the greater electronic stability compared to the phases with noncollinear JT arrangements, and 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 > 3 eV, computed using HSE06 and $G_0W_0$.

cond-mat.mtrl-sci↗

arXiv2025

Cross-functional transferability in universal machine learning interatomic potentials

The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy surface. In principle, the accuracy of uMLIPs can be further improved by bridging the model from lower-fidelity datasets to high-fidelity ones. In this work, we analyze the challenge of this transfer learning problem within the CHGNet framework. We show that significant energy scale shifts and poor correlations between GGA and r$^2$SCAN pose challenges to cross-functional data transferability in uMLIPs. By benchmarking different transfer learning approaches on the MP-r$^2$SCAN dataset of 0.24 million structures, we demonstrate the importance of elemental energy referencing in the transfer learning of uMLIPs. By comparing the scaling law with and without the pre-training on a low-fidelity dataset, we show that significant data efficiency can still be achieved through transfer learning, even with a target dataset of sub-million structures. We highlight the importance of proper transfer learning and multi-fidelity learning in creating next-generation uMLIPs on high-fidelity data.

cond-mat.mtrl-sci↗

arXiv2025

A practical guide to machine learning interatomic potentials -- Status and future

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years.

cond-mat.mtrl-sci↗

arXiv2025

Effect of cation-disorder on lithium transport in halide superionic conductors

Li$_2$ZrCl$_6$ (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 non-equilibrium 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 the long-term stability of their properties.

cond-mat.mtrl-sci↗

arXiv2025

A Foundational Potential Energy Surface Dataset for Materials

Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density functional theory (DFT)$^4$ for PES modeling across the periodic table. However, their accuracy today is fundamentally constrained due to a reliance on DFT relaxation data.$^{5,6}$ Here, we introduce MatPES, a foundational PES dataset comprising $\sim 400,000$ structures carefully sampled from 281 million molecular dynamics snapshots that span 16 billion atomic environments. We demonstrate that UMLIPs trained on the modestly sized MatPES dataset can rival, or even outperform, prior models trained on much larger datasets across a broad range of equilibrium, near-equilibrium, and molecular dynamics property benchmarks. We also introduce the first high-fidelity PES dataset based on the revised regularized strongly constrained and appropriately normed (r$^2$SCAN) functional$^7$ with greatly improved descriptions of interatomic bonding. The open source MatPES initiative emphasizes the importance of data quality over quantity in materials science and enables broad community-driven advancements toward more reliable, generalizable, and efficient UMLIPs for large-scale materials discovery and design.

cond-mat.mtrl-sci↗

arXiv2025

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 a collection of atoms, 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, our intention is for MatGL to be an extensible ``batteries-included'' library for the development of advanced graph deep learning models 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 includes a variety of pre-trained universal interatomic potentials (aka ``foundational materials models (FMM)'') and property prediction models are also included for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL includes support for Pytorch Lightning for rapid training of models.

cond-mat.mtrl-sci↗

arXiv2025

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 $16d$ 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.

cond-mat.mtrl-sci↗

arXiv2025

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pair-wise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here, we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through "liquid-like" product layers into a thermodynamic cellular reaction model. In the Ba-Ti-O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion-thermodynamic interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length and time scales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

cond-mat.mtrl-sci↗

arXiv2025

Synthetic accessibility and sodium ion conductivity of the Na$_{8-x}$A$^{x}$P$_2$O$_9$ (NAP) high-temperature sodium superionic conductor framework

Advancement of solid state electrolytes (SSEs) for all solid state batteries typically focuses on modification of a parent structural framework for improved conductivity, \textit{e.g.} cation substitution for an immobile ion or varying the concentration of the mobile ion. Therefore, novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, and unlocking optimal conductors with different properties (\textit{e.g.} mechanical properties, sintering needs, electrochemical stability) than previously published. Herein, we perform a high throughput survey of an understudied structural framework for sodium ion conduction, Na$_{8-x}$A$^x$P$_2$O$_9$ (NAP), to understand the family's thermodynamic stability, synthesizability, and ionic conduction. We first show that the parent phase Na$_4$TiP$_2$O$_9$ (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons from a pseduo-Jahn Teller mode in the 1D titanium chains. Then, screening of cation-substituted structural candidates with \textit{ab initio} and machine-learned potential calculations reveal a number of candidates that are thermodynamically stable, likely synthesizable, and have high predicted ionic conductivities. High throughput experimental trials and subsequent methodology optimization of one Na$_4$SnP$_2$O$_9$ (NSP) highlight collective challenges to the synthesis pathways for sodium phosphate materials via solid state synthesis. Our results demonstrate that NAP is a highly tunable conduction framework whose high temperature conductivity transition has heretofore eliminated it from significant research interest. By expanding the structural toolkit for SSE design, we increase the number of useful sodium ion electrolytes for integration into safe and accessible solid state batteries.

cond-mat.mtrl-sci↗

arXiv2024

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery, an evaluation framework for ML energy models, applied as pre-filters for high-throughput searches of stable inorganic crystals. This framework addresses the disconnect between thermodynamic stability and formation energy, as well as retrospective vs. prospective benchmarking in materials discovery. We release a Python package to support model submissions and maintain an online leaderboard, offering insights into performance trade-offs. To identify the best-performing ML methodologies for materials discovery, we benchmarked various approaches, including random forests, graph neural networks (GNNs), one-shot predictors, iterative Bayesian optimizers, and universal interatomic potentials (UIP). Our initial results rank models by test set F1 scores for thermodynamic stability prediction: EquiformerV2 + DeNS > Orb > SevenNet > MACE > CHGNet > M3GNet > ALIGNN > MEGNet > CGCNN > CGCNN+P > Wrenformer > BOWSR > Voronoi fingerprint random forest. UIPs emerge as the top performers, achieving F1 scores of 0.57-0.82 and discovery acceleration factors (DAF) of up to 6x on the first 10k stable predictions compared to random selection. We also identify a misalignment between regression metrics and task-relevant classification metrics. Accurate regressors can yield high false-positive rates near the decision boundary at 0 eV/atom above the convex hull. Our results demonstrate UIPs' ability to optimize computational budget allocation for expanding materials databases. However, their limitations remain underexplored in traditional benchmarks. We advocate for task-based evaluation frameworks, as implemented here, to address these limitations and advance ML-guided materials discovery.

cond-mat.mtrl-sci↗

arXiv2024

Voltage Mining for (De)lithiation-stabilized Cathodes and a Machine Learning Model for Li-ion Cathode Voltage

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyaion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulae is constructed, and shows state-of-the-art performance when compared to two established composition-based ML models for materials properties predictions, Roost and CrabNet.

cond-mat.mtrl-sci↗

arXiv2024

AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.

cond-mat.mtrl-sci↗

arXiv2024

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have seen the emergence of universal MLIPs (uMLIPs) that are pre-trained on diverse materials datasets, providing opportunities for both ready-to-use universal force fields and robust foundations for downstream machine learning refinements. 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 under-prediction in a series of atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, phonon vibration modes, ion migration barriers, and general high-energy states. We find that the PES softening behavior originates from a systematic underprediction error of the PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. We demonstrate that the PES softening issue can be effectively rectified by fine-tuning with a single additional data point. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. This result rationalizes the data-efficient fine-tuning performance boost commonly observed with foundational MLIPs. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.

cond-mat.mtrl-sci↗

arXiv2024

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. In this study, 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 five 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 automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal model trained on diverse chemistries, 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.

cond-mat.mtrl-sci↗

arXiv2023

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3,520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO$_3$). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82,985 possible BaTiO$_3$ synthesis reactions and selected nine for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl$_2$ and Na$_2$TiO$_3$) that produce BaTiO$_3$ faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

cond-mat.mtrl-sci↗

arXiv2023

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 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 the synthesis of many novel materials through selective polymorph nucleation.

cond-mat.mtrl-sci↗