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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Paradigms of frustration in superionic solid electrolytes

Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. In this Perspective, we draw upon atomistic simulations of a wide range of superionic conductors to illustrate some ways frustration can lower diffusion cation barriers in solids. Based on our studies of halides, oxides, sulfides and hydroborates and a survey of published reports, we classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape. These include chemical frustration, which derives from competing factors in the anion–cation interaction; structural frustration, which arises from lattice arrangements that induce site distortion or prevent cation ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion reorientation or cation reconfiguration. For each class of frustration, we provide detailed simulation analyses of various materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. We propose the use of these categories as a general construct for classifying frustration in superionic conductors and discuss implications for future development of suitable descriptors and improvement strategies.

25 ENERGY STORAGE↗
DOE OSTI2024

Magnesium ions mitigate metastable states in the regulatory landscape of mRNA elements

Residing in the 5' untranslated region of the mRNA, the 2'-deoxyguanosine (2'-dG) riboswitch mRNA element adopts an alternative structure upon binding of the 2'-dG molecule, which terminates transcription. RNA conformations are generally strongly affected by positively charged metal ions (especially Mg 2+ ). We have quantitatively explored the combined effect of ligand (2'-dG) and Mg 2+ binding on the energy landscape of the aptamer domain of the 2'-dG riboswitch with both explicit solvent all-atom molecular dynamics simulations (99 μsec aggregate sampling for the study) and selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments. We show that both ligand and Mg 2+ are required for the stabilization of the aptamer domain; however, the two factors act with different modalities. The addition of Mg 2+ remodels the energy landscape and reduces its frustration by the formation of additional contacts. In contrast, the binding of 2'-dG eliminates the metastable states by nucleating a compact core for the aptamer domain. Mg 2+ ions and ligand binding are required to stabilize the least stable helix, P1 (which needs to unfold to activate the transcription platform), and the riboswitch core formed by the backbone of the P2 and P3 helices. Mg 2+ and ligand also facilitate a more compact structure in the three-way junction region.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Stimulus-Responsive Modulation of Solvation Environments in Solid Catalysts

Liquid environments play a crucial role in the biological processes occurring in living organisms as well as in many human-made processes involving electrochemistry, photo-, and thermocatalysis. In the majority of these systems, aqueous phases are ubiquitous due to water’s natural abundance. Water molecules, however, can exert large changes in the chemical environment of catalytically active sites, altering the reaction rates, selectivity, and catalyst stability. These solvation effects induced by water molecules near catalytic sites can drastically change the energy landscape and unlock unique reaction pathways with far more favorable kinetics. In nature, living organisms couple these complex interactions with detection, communication, and actuation mechanisms to induce self-regulatory behavior, ensuring stability of the system and thus long-term durability. Extrapolating this behavior to heterogeneous catalysis is desirable because the resulting “smart materials” can potentially unlock new chemical conversion processes with higher atom efficiency, rates, and stability. The combination of polymer chemistry and heterogeneous catalysis has introduced versatile approaches for creating materials that can respond to cues in the reaction medium that alter the accessibility, intrinsic activity, and selectivity of the catalyst. To achieve this, one could combine stimulus-responsive polymers, which undergo a large volumetric phase transition in response to an external stimulus, with a solid catalyst. This chemo-mechanical response has been employed to create a variety of nanoreactor vessels with stimulus-responsive character that turn on- and off- depending on the reaction conditions. In this Account, we focus on the impact of these polymer coatings on the solvation environment around the active site and the implications of these effects on the reaction energy landscape, molecular arrangement of the solvent, electric fields at the catalyst–liquid interface, binding energy, and mobility of surface reaction intermediates. These seemingly subtle changes in solvent molecules induced by the presence of polymers can have a tremendous impact on the development of bioinspired heterogeneous catalysts, reliable chemical clocks, micro/nanoreactors, and robots. The large library of polymer chemistries offers a plethora of combinations of stimulus-responsive mechanisms (e.g., temperature, pH, light, magnetic field, solvent composition), providing the possibility of creating homeostatic catalysts à la carte.

catalysts↗
DOE OSTI2020

Order-disorder versus displacive transitions in Jahn-Teller active layered materials

Large anharmonic vibrations often play a crucial role in dynamically stabilizing crystalline phases whose structures are unstable at low temperature. Although the average structure of such phases can be measured through diffraction experiments, their local structure remains a challenge to characterize and understand. Dynamically stabilized phases are often classified as order/disorder or displacive based on the qualitative nature of their local structure. A robust understanding of how chemistry determines this distinction in behavior, however, is lacking. This article presents a parametric study of an anharmonic vibrational model that describes the transition from a cooperative to a noncooperative Jahn-Teller distortion in layered oxides—a class of materials widely used in Li-ion and Na-ion batteries. The results illustrate how the shape of the energy landscape determines the extent to which the high-temperature phase has order/disorder vs displacive character. In this work, we find that the nature of the high-temperature phase is determined by a competition between the strength of the elastic coupling between Jahn-Teller distortions at nearby sites and the energy scale driving the Jahn-Teller distortion. A comparison of the model to energy landscapes calculated with density-functional theory suggests that the high-temperature phases of the Jahn-Teller active layered compounds LiNiO 2 , NaNiO 2 , LiMnO 2 , and NaMnO 2 exhibit order/disorder character.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The energetics and dynamics of confinement in flexible frameworks and molecular confinement

Porous frameworks form the chemical and structural basis for critical technologies in separations, catalysis, nuclear waste containment and biomedical applications. Hundreds of zeolites and metal organic frameworks (MOFs) have been synthesized, and their ability to separate and store hydrogen, methane and carbon dioxide has been investigated both experimentally and theoretically. Nevertheless, a fundamental and systematic molecular-level understanding of the thermodynamic and structural factors governing the stability and guest-host interactions in these materials lags behind focused studies of specific systems. Because the guest molecules interact with each other and with the host framework, molecular confinement is a finely balanced and complex phenomenon. The ability of the guest molecules to bind and diffuse through the pores is determined by the nature of the host framework which, in turn, responds to the nature and concentration of guest molecules and to pressure and temperature. The work explores how framework flexibility, tailored by structure, composition, temperature and pressure, is a general phenomenon, similar in nature but variable in extent, in both zeolites and MOFs and is part of a free energy landscape in which framework-guest interactions, pressure, and temperature result in changes in framework geometry and, in some cases, phase transitions. These subtle and/or pronounced changes in lattice geometry, energetics, and dynamics can play a decisive role in confinement and in differentiating the binding of molecules of similar size. The free energy landscape created by these structural changes links polymorphism, amorphization, “breathing,” “gate opening” and confinement. Specifically, the generality of such behavior arises from commonalities in lattice dynamics and energetics of frameworks containing a combination of strong rigid bonds and weaker more flexible deformation modes. Identifying and describing these common and collective phenomena is the focus of the research on a selected group of zeolites and MOFs. Structural studies using X-ray and neutron diffraction explore the mechanical functionality of these important materials, specifically how framework materials respond to changes in temperature, pressure and guest loading. These structural studies are combined with calorimetric measurements, using techniques uniquely developed in the participating laboratories, of heats of formation, heat capacities and entropies, and guest-host interactions. The experimental thermodynamic studies are complemented by inelastic neutron scattering studies of the lattice dynamics related both to framework vibrations and to guest-host interactions. The report below summarizes the work done at UC Davis, which is complemented by work done at BYU (Woodfield) and Virginia Tech (Ross).

36 MATERIALS SCIENCE↗
DOE OSTI2022

The energetics and dynamics of confinement in flexible frameworks and molecular confinement

Porous frameworks form the chemical and structural basis for critical technologies in separations, catalysis, nuclear waste containment and biomedical applications. Hundreds of zeolites and metal organic frameworks (MOFs) have been synthesized, and their ability to separate and store hydrogen, methane and carbon dioxide has been investigated both experimentally and theoretically. Nevertheless, a fundamental and systematic molecular-level understanding of the thermodynamic and structural factors governing the stability and guest-host interactions in these materials lags behind focused studies of specific systems. Because the guest molecules interact with each other and with the host framework, molecular confinement is a finely balanced and complex phenomenon. The ability of the guest molecules to bind and diffuse through the pores is determined by the nature of the host framework which, in turn, responds to the nature and concentration of guest molecules and to pressure and temperature. The work explores how framework flexibility, tailored by structure, composition, temperature and pressure, is a general phenomenon, similar in nature but variable in extent, in both zeolites and MOFs and is part of a free energy landscape in which framework-guest interactions, pressure, and temperature result in changes in framework geometry and, in some cases, phase transitions. These subtle and/or pronounced changes in lattice geometry, energetics, and dynamics can play a decisive role in confinement and in differentiating the binding of molecules of similar size. The free energy landscape created by these structural changes links polymorphism, amorphization, “breathing,” “gate opening” and confinement. Specifically, the generality of such behavior arises from commonalities in lattice dynamics and energetics of frameworks containing a combination of strong rigid bonds and weaker more flexible deformation modes. Identifying and describing these common and collective phenomena is the focus of the research on a selected group of zeolites and MOFs. Structural studies using X-ray and neutron diffraction explore the mechanical functionality of these important materials, specifically how framework materials respond to changes in temperature, pressure and guest loading. These structural studies are combined with calorimetric measurements, using techniques uniquely developed in the participating laboratories, of heats of formation, heat capacities and entropies, and guest-host interactions. The experimental thermodynamic studies are complemented by inelastic neutron scattering studies of the lattice dynamics related both to framework vibrations and to guest-host interactions. This research was a collaboration between UC Davis (A. Navrotsky) BYU (B. Woodfield) and Virginia Tech (N. Ross).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Non-dilute helium-related defect interactions in the near-surface region of plasma-exposed tungsten

We report a systematic energetic analysis of helium-related defect interactions that mediate helium (He) segregation on surfaces of plasma-exposed tungsten at different levels of He ion implantation. We focus on high He fluences that increase the He content in the plasma-exposed material well beyond the dilute limit of He concentration and employ atomic configurations generated by large-scale molecular dynamics simulations of He-implanted tungsten. We perform systematic molecular statics computations of cluster– defect interaction energetics in the highly defect-rich near-surface region of plasma-exposed tungsten for small mobile helium clusters as a function of the cluster distance from the surface. In this region, mobile clusters are also subjected to the stress fields generated by defects such as helium bubbles and other clusters, which govern cluster–defect interactions in addition to the cluster–surface interaction. Based on systematic investigation of individual cluster–defect interactions, we develop a mathematical framework to describe the interaction energy landscapes consisting of elastic interaction potential perturbations to finite-width square-well potentials, where the potential well accounts for cluster trapping by the defect at close range and subsequent coalescence and the perturbation potential is parameterized according to elastic inclusion theory. Superposition of all the relevant interaction potentials provides a comprehensive description of the interaction energy landscape that would be experienced by a small mobile cluster along its migration path toward the plasma-exposed surface at high He fluence. Such descriptions are particularly important for developing atomistically-informed, hierarchical multi-scale models of helium cluster dynamics in plasma-facing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗
DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ion-Assisted Nanoscale Material Engineering in Atomic Layers

Achieving deterministic control over the properties of low-dimensional materials with nanoscale precision is a long-sought goal. Mastering this capability has a transformative effect on the design of multifunctional electrical and optical devices. Here, we present an ion-assisted synthetic technique that enables precise control over the material composition and energy landscape of two-dimensional (2D) atomic crystals. Our method transforms binary transition-metal dichalcogenides, like MoSe2, into ternary MoS2αSe2(1-α) alloys with systematically adjustable compositions, α. By piecewise assembly of the lateral, compositionally modulated MoS2αSe2(1-α) segments within 2D atomic layers, we present a synthetic pathway toward the realization of multicompositional designer materials. Our technique enables the fabrication of advanced 2D structures with arbitrary boundaries, dimensions as small as 30 nm, and fully customizable energy landscapes. Our optical characterizations further showcase the potential for implementing tailored optoelectronics in these engineered 2D crystals.

2D materials↗
DOE OSTI2023

Models of dislocation glide and strengthening mechanisms in bcc complex concentrated alloys

Abstract The mechanical response of complex concentrated alloys (CCAs) deviates from that of their pure and dilute counterparts due to the introduction of a combinatorially sized chemical concentration dimension. Compositional fluctuations constantly alter the energy landscape over which dislocations move, leading to line roughness and the appearance of defects such as kinks and jogs under stress and temperature conditions where they would ordinarily not exist in pure metals and dilute alloys. The presence of such chemical defects gives rise to atomic-level mechanisms that fundamentally change how CCAs deform plastically at meso- and macroscales. In this article, we provide a review of recent advances in modeling dislocation glide processes in CCAs, including atomistic simulations of dislocation glide using molecular dynamics, kinetic Monte Carlo simulations of edge and screw dislocation motion in refractory CCAs, and phase-field models of dislocation evolution over complex energy landscapes. We also discuss pathways to develop comprehensive simulation methodologies that connect an atomic-level description of the compositional complexity of CCAs with their mesoscopic dislocation-mediated plastic response with an eye toward improved design of CCA with superior mechanical response. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2026

Pressure-tuned plethora of ferroelectric phases in CuInP 2 S 6

Layered chalcogenides like CuInP 2 S 6 are room temperature ferroelectrics. Modest compression even increases the electric polarization, raising questions about the origin of this unusual trend and other properties under pressure. In this work, we combine synchrotron-based infrared and Raman scattering spectroscopies, x-ray diffraction, and resistivity measurements with first-principles calculations of the lattice dynamics and energy landscape to unravel the influence of pressure on symmetry, polarization, and metallicity. We uncover a remarkable series of phase transitions across a series of polar space groups: monoclinic Cc → trigonal P31c (prismatic sulfur) → P31c (octahedral sulfur). True metallicity develops above 63 GPa, significantly higher than in related MPS 3 materials (M = Mn, Co, Fe, Ni), offering a picture of competing states of matter that is different than previously supposed. Detailed examination of pressure trends within the Cc phase also reveals phonon lifetime changes and streaking of satellite x-ray peaks that correlate with the maximum polarization. We discuss these tendencies in terms of Cu + ion migration, phase formation, and the overall energy landscape. Our findings place the high pressure behavior of CuInP 2 S 6 on a firm foundation and pave the way for the development of structure-property relations in this family of complex chalcogenides.

ferroelectric phases↗
DOE OSTI2025

Trends and 2025 Insights on the Rise of Electric Vehicles in the USA

Plug-in electric vehicles (EVs) are reshaping the transportation energy landscape, providing a practical alternative to petroleum fuels for a growing number of applications. EV sales grew 55x in the past decade (2014-2024) and 6x since 2020, driven by technological progress enabled by policies to reduce transportation emissions as well as industrial plans motivated by strategic value of EVs for global competitiveness, jobs and geopolitics. In 2024, 22% of passenger cars sold globally were EVs and opportunities for EVs beyond on-road applications are growing, including solutions to electrify off-road vehicles, maritime and aviation. This Review updates and expands our 2020 assessment of the scientific literature and describes the current status and future projections of EV markets, charging infrastructures, vehicle-grid integration and supply chains in the USA. EV is the lowest-emission motorized on-road transportation option, with life-cycle emissions decreasing as electricity emissions continue to decrease. Charging infrastructure grew in line with EV adoption but providing ubiquitous reliable and convenient charging remains a challenge. EVs are reducing electricity costs in several US markets and coordinated EV charging can improve grid resilience and reduce electricity costs for all consumers. The current trajectory of technology improvement and industrial investments points to continued acceleration of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Towards an Understanding of Oxidative Damage in an a-L-Arabinofuranosidase of Trichoderma reesei : a Molecular Dynamics Approach

Trichoderma reesei is a ‘workhorse’ fungus that produces glycosyl hydrolases (e.g., cellulases) at high titers for use in industrial bioprocessing. In this study, we focused on two side-chain hemicellulases, a-L-arabinofuranosidases and a-D-galactosidases, which are important for the treatment of lignocellulosic biomass, but susceptible to oxidative damage that can occur during industrial processing. The molecular details that render these enzymes inactive have not yet been identified. To approach this issue, we used proteomics to identify amino acid residues that were oxidized after a relevant oxidative treatment (Fenton reaction). These oxidative modifications were included in the 3D-protein structures, and using molecular dynamics simulations, we then studied the behaviors of non-modified and oxidized enzymes. These simulations showed significant alterations of the conformational stability of proteins when oxidized, as evidenced by changes in root mean square deviation (RMSD) and principal component analyses (PCA) trajectories. Likewise, enzyme-ligand interactions such as hydrogen bonds were greatly reduced in quantity and quality in the oxidized proteins. Finally, free energy landscape plots showed that there was a more rugged energy surface in the oxidized proteins, implying a less favorable reaction pathway. These results reveal the basis for loss of function in these carbohydrate active enzymes (CAZYs) in T. reesei.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Efficient single-photon emission via quantum-confined charge funneling to quantum dots

Quantum light sources, particularly single-photon emitters (SPEs), are critical for quantum communications and computing. Among them, III-V semiconductor quantum dots (QDs) have demonstrated superior SPE metrics, including near-unity brightness, high photon purity, and indistinguishability, making them especially suitable for quantum applications. However, their overall quantum efficiency—determined by a product of the internal, excitation, and outcoupling efficiencies—remains limited, primarily due to low (typically below 0.1%) excitation efficiency. To mitigate the low efficiency under non-resonant pumping, here we realize liquid droplet etched GaAs QDs in a microscale 3D AlGaAs charge-carrier funnel. The funnel channels charge carriers to the QD and enhances the overall emission efficiency by over one order of magnitude while preserving the SPE behavior. We reveal that a modified energy landscape around the QD leads to the excitation efficiency improvement. These energy landscape-modified QDs can be operated with optical excitation up to 10 μm away, raising the promise of efficient electrically driven QD SPEs for quantum information systems.

Park, Sanghyeok [Sandia National Laboratories (SNL↗
DOE OSTI2023

The Linkage Between Electro-Chemical Mechanical Instabilities in Battery Materials

Battery chemistry must be diversified to achieve a sustainable energy landscape by effectively utilizing renewable energy sources. Alkali metal-ion, all-solid-state, metal–air batteries, and multivalent batteries offer unique cost, safety, raw material abundance, energy, and power density solutions. However, realizing these “beyond Li-ion batteries” must uncover their working principles and performance & property relationships. In this aspect, mitigating chemo-mechanical instabilities in the structure and surface of the electrodes plays a crucial role in their performance. Unfortunately, the coupling between electrochemical and mechanical interactions is often poorly understood due to a lack of operando characterization. Here, this review article explains the working principles of curvature measurement and digital image correlation for measuring stress and strain generations in battery materials. We provided specific examples of how these operando mechanical measurements shed light on instabilities in alkali metal ion electrodes, solid electrolytes, Li-O 2 batteries, and aqueous Zn-ion batteries. Operando mechanical measurements offer an effective way to map changes in the physical fingerprint of the battery materials, therefore providing crucial information to elucidate instabilities in battery materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?

Despite decades of study, the identity of the dominant Li + -conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/Li 2 O/Li 2 CO 3 nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (LiPO 2 F 2 ), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature σ ≈ 0.18 mS cm –1 and E a ≈ 0.40 eV. Here, this enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li-P-O-F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces.

Zhong, Peichen [University of California, Berkeley↗