Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cell heterogeneity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Continuous Production of Carbon-Supported and Surfactant-Free Pt-M (M=Fe, Co, Ni, and Cu) Nanocrystals for Catalyzing Oxygen Reduction

Scalable production of carbon-supported Pt-M (M=Co, Ni, and Fe) alloy nanocrystals is of great importance for their practical application as catalysts towards the oxygen reduction reaction (ORR), a process key to the operation of proton-exchange membrane fuel cells. Here we report the use of a fluidic device for the in situ nucleation and growth of Pt-M nanocrystals on a commercial carbon support in a continuous and scalable fashion. The use of dimethylformamide not only enables well dispersion of the carbon powders for the creation of a homogeneous reaction mixture but also helps reduce metal precursors for the heterogeneous nucleation and growth of nanocrystals on the carbon surface. The size, shape, and composition of the nanocrystals can all be tuned by changing the metal precursors added into the reaction mixture, resulting in Pt-M nanocrystals uniformly distributed across the surface of the carbon support. Among the nanocrystals, the carbon-supported Pt-Co nanocrystals show the highest ORR specific and mass activities at 0.9 V, demonstrating 11.4- and 8.8-fold enhancements over the state-of-the-art commercial Pt/C catalyst.

Electrochemistry↗
NASA NTRS2023

An Enabling Platform for Achieving Multiscale Multiphysics Analysis of Multiphase Materials

This paper will address current NASA GRC research efforts utilizing the NASA Multiscale Analysis Tool (NASMAT) which address technical gaps in three of the Vision 2040 key discipline areas. NASMAT is a versatile platform for performing computationally efficient multiscale analyses of heterogeneous materials and is available free through the NASA software catalog. It offers the user flexibility to define an arbitrary number of length scales (levels) where a variety of micromechanics theories can be implemented at each level. Micromechanics theories can be selected to balance accuracy and computational efficiency and range from analytical (Mori-Tanaka) to several semi-analytical (method of cells) formulations. The resulting anisotropic, evolving nonlinear, thermomechanical constitutive model can also be coupled with external software and used to perform multiscale analyses of more complex structures. A recent application to model a complex, three-dimensional woven composite, with a particular emphasis placed on multiscale measurements utilized to enhance the quality of the multiscale analysis will be discussed. Since typical NASMAT analyses can be completed on the order of seconds to minutes, a second example will demonstrate the ability to generate large quantities of data useful for sensitivity analysis, uncertainty quantification, or machine learning applications. Current progress on implementing NASMAT within a multiscale digital thread/digital twin framework will also be addressed. These examples will demonstrate that NASMAT is an enabling platform for achieving the goals in Vision 2040.

Multiscale↗
DOE OSTI2022

IL-17A Increases Doxorubicin Efficacy in Triple Negative Breast Cancer

Due to lack of targetable receptors and intertumoral heterogeneity, triple negative breast cancer (TNBC) remains particularly difficult to treat. Doxorubicin (DOX) is typically used as nonselective neoadjuvant chemotherapy, but the diversity of treatment efficacy remains unclear. Comparable to variability in clinical response, an experimental model of TNBC using a 4T1 syngeneic mouse model was found to elicit a differential response to a seven-day treatment regimen of DOX. Single-cell RNA sequencing identified an increase in T cells in tumors that responded to DOX treatment compared to tumors that continued to grow uninhibited. Additionally, compared to resistant tumors, DOX sensitive tumors contained significantly more CD4 T helper cells (339%), γδ T cells (727%), Naïve T cells (278%), and activated CD8 T cells (130%). Furthermore, transcriptional profiles of tumor infiltrated T cells in DOX responsive tumors revealed decreased exhaustion, increased chemokine/cytokine expression, and increased activation and cytotoxic activity. γδ T cell derived IL-17A was identified to be highly abundant in the sensitive tumor microenvironment. IL-17A was also found to directly increase sensitivity of TNBC cells in combination with DOX treatment. In TNBC tumors sensitive to DOX, increased IL-17A levels lead to a direct effect on cancer cell responsiveness and chronic stimulation of tumor infiltrated T cells leading to improved chemotherapeutic efficacy. IL-17A’s role as a chemosensitive cytokine in TNBC may offer new opportunities for treating chemoresistant breast tumors and other cancer types.

4T1↗
DOE OSTI2021

Therapeutic targeting of membrane-associated proteins in central nervous system tumors

The activity of the most complex system, the central nervous system (CNS) is profoundly regulated by a huge number of membrane-associated proteins (MAP). A minor change stimulates immense chemical changes and the elicited response is organized by MAP, which acts as a receptor of that chemical or channel enabling the flow of ions. Slight changes in the activity or expression of these MAPs lead to severe consequences such as cognitive disorders, memory loss, or cancer. CNS tumors are heterogeneous in nature and hard-to-treat due to random mutations in MAPs; like as overexpression of EGFRvIII/TGFβR/VEGFR, change in adhesion molecules α5β3 integrin/SEMA3A, imbalance in ion channel proteins, etc. Extensive research is under process for developing new therapeutic approaches using these proteins such as targeted cytotoxic radiotherapy, drug-delivery, and prodrug activation, blocking of receptors like GluA1, developing viral vector against cell surface receptor. The combinatorial approach of these strategies along with the conventional one might be more potential. Henceforth, our review focuses on in-depth analysis regarding MAPs aiming for a better understanding for developing an efficient therapeutic approach for targeting CNS tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Chromosome 8 gain is associated with high-grade transformation in MPNST

One of the most common malignancies affecting adults with Neurofibromatosis type 1 (NF1) is the malignant peripheral nerve sheath tumor (MPNST), an aggressive and often fatal sarcoma which most often arises from within benign plexiform neurofibromas. Despite advances in our understanding of the pathobiology of MPNST, there are few effective therapeutic options, and no investigational agents have proven successful in clinical trials. To further understand the genomic heterogeneity of MPNST, and to generate a preclinical platform that encompasses this heterogeneity, we developed a collection of NF1-MPNST patient derived xenografts (PDX). These PDX were compared to the primary tumors from which they were derived using copy number analysis, whole exome and RNA sequencing. We identified chromosome 8 gain as a recurrent genomic event in MPNST and validated its occurrence by FISH in the PDX and parental tumors, in a validation cohort, and by single cell sequencing in the PDX. Finally, we show that chromosome 8 gain is associated with inferior overall survival in soft tissue sarcomas. Taken together, these data suggest that chromosome 8 gain is a critical event in MPNST pathogenesis, and may account for the aggressive nature and poor outcomes in this sarcoma subtype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Phase-field modeling of radiation-induced composition redistribution: An application to additively manufactured austenitic Fe–Cr–Ni

Multicomponent alloys undergoing irradiation damage develop radiation-induced composition redistribution at point defect sinks such as grain boundaries (GBs) and dislocations. Such redistribution results in undesired changes to their mechanical behavior and corrosion resistance. Additively manufactured alloys proposed for future nuclear applications are expected to demonstrate a distinct response to irradiation owing to their unique microstructure with as-solidified dislocation density and chemical microsegregation. To capture the composition redistribution in such systems, we develop a mesoscale model with coupled evolution of atomic and point defect components in the presence of dislocation density, dislocation heterogeneity, and thermodynamic interactions at the GB. The model is parameterized for an FCC Fe–Cr–Ni alloy as a representative system for austenitic stainless steels, and simulations are performed in 1D and 2D as a function of irradiation temperature, dose, dislocation density, and grain size. Radiation-induced segregation (RIS) characterized by Cr depletion and Ni enrichment is predicted at both the GB and the dislocation cell wall, with RIS being lower in magnitude but wider at the cell wall. Strongly biased absorption of self-interstitials by dislocations is found to suppress Ni enrichment but slightly enhance Cr depletion under certain conditions. Thermodynamic segregation at the GB is predicted to be narrower and opposite in sign to RIS for both Cr and Ni. Importantly, non-monotonic segregation is found to occur when both thermodynamic and RIS mechanisms are considered, providing a novel physical interpretation of experimental observations. The model is expected to serve as a key tool in accelerated qualification of irradiated materials.

additively manufactured microstructure↗
DOE OSTI2021

Molecularly tunable heterogeneous catalysts by edge functionalization of graphitic carbons

Disclosed are surface immobilized (electro)catalysts that may be prepared by a condensation reaction that generates an aromatic unit that is robust to acid and base and elevated temperatures. Among their many desirable characteristics, the catalysts are far less prone to the bimolecular deactivation pathways commonly observed for homogeneous catalysts, and may be used in solvents with a range of polarities and dielectric strengths. The catalysts are suitable for a wide array of thermal catalytic reactions (polymerization, oxidation, hydrogenation, cross-coupling etc.) and as anodes and/or cathodes in fuel cells, electrolyzers, and in batteries and supercapacitors.

Surendranath, Yogesh↗
DOE OSTI2022

ORNL_AISD_NiNb

This dataset describes the nickel-niobium solid solution binary alloy, where the two constituent elements nickel (Ni) and niobium (Nb) are randomly placed on an underlying crystal lattice. This dataset for nickel-niobium (Ni-Nb) alloys available includes the formation energy and bulk modulus for each crystal structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). The geometry optimization ensures that all the alloy samples reached the equilibrium with negative formation energy. We perform geometry optimizations using the LAMMPS simulation package [1], a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. We utilized the embedded atom model (EAM) potential for Ni and Nb developed in a previous study [2]. The potential could describe behaviors of the liquid and solid phases of Ni-Nb alloy. The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Ã… for FCC, 3.32 Ã… for BCC, and 3.5 Ã… for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. The dataset consists of three sets of crystal structures. The first set contains 46,086 irregular crystal structures, each of them with 54 atoms, obtained through optimization starting from a regular BCC crystal structure. The second set contains 24,543 irregular crystal structures, each of them with 32 atoms, obtained through optimization starting from a regular FCC crystal structure. The third set contains 39,303 irregular crystal structures, each of them with 48 atoms, obtained through optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been unified in a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. Organization of files inside the dataset: the dataset contains three subdirectories called • BCC_opt • FCC_opt • HCP_opt based on the type of initial regular structure used to start the geometry optimization. Inside each of these folders, every atomic structure is identified by a string “A_B_Câ€, where A denotes the number of Nb in the system, B denotes index of structure with a given Nb number, and C denotes the total number of structures generated with a given Nb number. For each optimized crystal structure identified by the unique string of characters “A_B_Câ€, three files are provided: • A_B_C_opt.xyz: The optimized geometries in xyz format • A_B_C_opt.cfg: The optimized geometries in cfg format. It includes cell information and atomic energy, and forces calculated from LAMMPS. • A_B_C.elastic: Raw data of 21 elastic constants from LAMMPS output. • A_B_C.bulk: Calculated upper and lower bounds of bulk modulus and averaged one based on Voigt-Reuss-Hill approach from *.elastic. References: [1] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 271:108171, 2022. [2] Y Zhang, R Ashcraft, MI Mendelev, CZ Wang, and KF Kelton. Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy. The Journal of chemical physics, 145(20):204505, 2016.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Localized Heterogeneous Nucleation for Vapor‐Assisted Sequential Deposition of Metal Halide Perovskites

Vapor-assisted hybrid two-step deposition, which combines thermally evaporated inorganic layers with solution-processed organic halides to form halide perovskites, has emerged as a scalable and industry-compatible route for textured tandem photovoltaics. However, this process is often hindered by reaction-limited phase formation, particularly when compact, non-porous, and highly crystalline inorganic layers formed by thermal evaporation restrict subsequent conversion, resulting in incomplete reaction and pronounced depth-dependent heterogeneity. In this study, we introduce a strategy to regulate the inorganic precursor layer by incorporating localized heterogeneous nucleation sites. Sparsely distributed hydrophilic metal oxide species serve as effective nucleation centers during vapor deposition, enabling effective control over film morphology and crystal orientation from the early stages of growth. This tailored inorganic framework facilitates the subsequent incorporation of organic halides, alleviating reaction limitations and suppressing residual unreacted precursors. Consequently, the perovskite films exhibit improved stoichiometric uniformity and enhanced optoelectronic quality, enabling wide-bandgap perovskite solar cells with markedly improved performance and operational stability. This work provides important mechanistic insight into crystal growth engineering of vapor-deposited perovskite thin films.

nucleation↗
DOE OSTI2025

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗
DOE OSTI2025

A Diffuse-Interface Model for Predicting the Evolution of Metallic Negative Electrodes and Interfacial Voids in Solid-State Batteries with Homogeneous and Polycrystalline Solid Electrolyte Separators

Here, this paper presents a novel diffuse-interface electrochemical model that simultaneously simulates the evolution of the metallic negative electrode and interfacial voids during the stripping and plating processes in solid-state batteries. The utility and validity of this model are demonstrated for the first time on a cell with a sodium (Na) negative electrode and a Na-β″-alumina ceramic solid electrolyte (SE) separator. Three examples are simulated. First, stripping and plating with a perfect electrode/electrolyte interface; second, stripping and plating with a single interfacial void at the electrode/electrolyte interface; third, stripping with multiple interfacial voids. Both homogeneous and polycrystalline SEs with low and high-conductivity grain boundaries (GBs) are considered for all three examples. Heterogeneous GB conductivity marginally impacts the thickness of the Na electrode in cases with a perfect electrode/electrolyte interface. Moreover, it results in local changes to void growth due to the interactions between the void edge and the GBs. The void growth rate is a linear function of the flux of Na atoms at the void edge, which in turn depends on the applied current density. We also show that the void coalescence rate increases with applied current density and can be marginally influenced by GB conductivity.

batteries↗
DOE OSTI2024

Insights into the action of phylogenetically diverse microbial expansins on the structure of cellulose microfibrils

Microbial expansins (EXLXs) are non-lytic proteins homologous to plant expansins involved in plant cell wall formation. Due to their non-lytic cell wall loosening properties and potential to disaggregate cellulosic structures, there is considerable interest in exploring the ability of microbial expansins (EXLX) to assist the processing of cellulosic biomass for broader biotechnological applications. Herein, EXLXs with different modular structure and from diverse phylogenetic origin were compared in terms of ability to bind cellulosic, xylosic, and chitinous substrates, to structurally modify cellulosic fibrils, and to boost enzymatic deconstruction of hardwood pulp. Five heterogeneously produced EXLXs (Clavibacter michiganensis; CmiEXLX2, Dickeya aquatica; DaqEXLX1, Xanthomonas sacchari; XsaEXLX1, Nothophytophthora sp.; NspEXLX1 and Phytophthora cactorum; PcaEXLX1) were shown to bind xylan and hardwood pulp at pH 5.5 and CmiEXLX2 (harboring a family-2 carbohydrate-binding module) also bound well to crystalline cellulose. Small-angle X-ray scattering revealed a 20–25% increase in interfibrillar distance between neighboring cellulose microfibrils following treatment with CmiEXLX2, DaqEXLX1, or NspEXLX1. Correspondingly, combining xylanase with CmiEXLX2 and DaqEXLX1 increased product yield from hardwood pulp by ~ 25%, while supplementing the TrAA9A LPMO from Trichoderma reesei with CmiEXLX2, DaqEXLX1, and NspEXLX1 increased total product yield by over 35%. This direct comparison of diverse EXLXs revealed consistent impacts on interfibrillar spacing of cellulose microfibers and performance of carbohydrate-active enzymes predicted to act on fiber surfaces. These findings uncover new possibilities to employ EXLXs in the creation of value-added materials from cellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2026

Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA

Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.

Liang, Jiakun [University of Hawai'i at Manoa] (OR↗
DOE OSTI2021

Stochastic simulations of temporally and spatially variable Fe cycling within a floodplain aquifer

This data package contains model input and output files for local and global sensitivity analysis (SA) of a reactive transport model simulating redox cycling within a floodplain aquifer. The reaction network is implemented in CrunchFlow and focuses on spatially and temporally heterogeneous Fe cycling, though it contains several other species and redox pathways (37 aqueous species and 7 minerals in total). The package contains data from 600 Monte Carlo simulations (for global SA) and 104 one-at-a-time simulations (for local SA). It includes all input files required to run the CrunchFlow model, as well as limited model output – .csv files containing time series of primary species concentrations at 21 grid cells within the domain. These time series files contain the following species: H+, CO32-, SO42-, Cl-, Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), HS-, CH3COO-, O2(aq), S(aq), and Br-.These data were used to demonstrate the application of a novel form of global SA, distance-based generalized sensitivity analysis (DGSA) to a reactive transport model. The 2D model simulates the export of reduced species from pockets of fine-grained, organic-rich sediments embedded in a coarse sand aquifer. Stochastic simulations jointly varied 17 key model input parameters across several orders of magnitude, including the spatial variability of each parameter. DGSA on the model results reveal that the amplitude and variability of exported Fe(II) are most sensitive to the interaction between sand permeability and individual reaction rates. By contrast, the propagation of reducing conditions downgradient from the fine-grained lenses depends most heavily on the rate of dissolved organic carbon and sulfate release from the fines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Composition and Pressure Effects on Partitioning of Ferrous Iron in Iron-Rich Lower Mantle Heterogeneities

Both seismic observations of dense low shear velocity regions and models of magma ocean crystallization and mantle dynamics support enrichment of iron in Earth’s lowermost mantle. Physical properties of iron-rich lower mantle heterogeneities in the modern Earth depend on distribution of iron between coexisting lower mantle phases (Mg,Fe)O magnesiowüstite, (Mg,Fe)SiO3 bridgmanite, and (Mg,Fe)SiO3 post-perovskite. The partitioning of iron between these phases was investigated in synthetic ferrous-iron-rich olivine compositions (Mg0.55Fe0.45)2SiO4 and (Mg0.28Fe0.72)2SiO4 at lower mantle conditions ranging from 33–128 GPa and 1900–3000 K in the laser-heated diamond anvil cell. The resulting phase assemblages were characterized by a combination of in situ X-ray diffraction and ex situ transmission electron microscopy. The exchange coefficient between bridgmanite and magnesiowüstite decreases with pressure and bulk Fe# and increases with temperature. Thermodynamic modeling determines that incorporation and partitioning of iron in bridgmanite are explained well by excess volume associated with Mg-Fe exchange. Partitioning results are used to model compositions and densities of mantle phase assemblages as a function of pressure, FeO-content and SiO2-content. Unlike average mantle compositions, iron-rich compositions in the mantle exhibit negative dependence of density on SiO2-content at all mantle depths, an important finding for interpretation of deep lower mantle structures.

Dorfman, Susannah M. (ORCID:0000000239689592)↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2024

Zero-Strain Metal-Insulator Transition by the Local Fluctuation of Cation Dimerization

The coupled electronic and structural transitions in metal-insulator transition (MIT) hinder ultrafast switching and ultimate endurance. Decoupling these transitions and achieving a zero-strain electronic MIT can overcome the fundamental limitations of MIT in solid materials. Here, this study demonstrates that iso-valent Ti dopants in supercooled VO 2 epitaxial films cause MIT with minimal hysteresis without changing unit-cell volume and crystal symmetry. The Ti dopants in the VO 2 lattice locally alter the configuration of V-V pairs, where the long-range ordering in V-V pairs is disrupted, and the nano-domains of V-V dimers are formed. Strikingly, these local V-V dimers persist even above the electronic transition temperature (T MI ), facilitating the zero-strain electronic MIT with nanoscale structural heterogeneity. The geometrically compatible interface between insulating and metallic phases drastically enhances switching speed and endurance during electrically and optically driven zero-strain MIT. In conclusion, this discovery offers a fresh perspective on the scientific understanding of MIT and the improved functionality in terms of device speed and reliability by decoupling electronic and structural transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kinetically Dormant Ni‐Rich Layered Cathode During High‐Voltage Operation

Abstract The degradation of Ni‐rich cathodes during long‐term operation at high voltage has garnered significant attention from both academia and industry. Despite many post‐mortem qualitative structural analyses, precise quantification of their individual and coupling contributions to the overall capacity degradation remains challenging. Here, by leveraging multiscale synchrotron X‐ray probes, electron microscopy, and post‐galvanostatic intermittent titration technique, the thermodynamically irreversible and kinetically reversible capacity loss is successfully deconvoluted in a polycrystalline LiNi 0.83 Mn 0.1 Co 0.07 O 2 cathode during long‐term charge/discharge cycling in full cell configuration. Contradicting the dramatic capacity loss, the layered structure remains highly alive even after 1000 cycles at 4.6 V while undergoing a three‐order of magnitude reduction in the mass transfer kinetics, leading to almost fully recoverable capacity under kinetic‐free conditions. Such kinetic dormant behavior after cycling is not simply ascribed to poor chemical diffusion by reconstructed cathode surface but highly synchronizes with the lattice strain evolution stemming from the structural heterogeneity between deeply delithiated layered and degraded rock‐salt phases at high voltage. These findings deepen the degradation mechanism of high‐voltage cathodes to achieve long‐cycling and fast‐charging performance.

36 MATERIALS SCIENCE↗