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At least 433 records · Page 24

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DOE OSTI2016

Nonlinear dynamics and instability of aqueous dissolution of silicate glasses and minerals

Aqueous dissolution of silicate glasses and minerals plays a critical role in global biogeochemical cycles and climate evolution. The reactivity of these materials is also important to numerous engineering applications including nuclear waste disposal. The dissolution process has long been considered to be controlled by a leached surface layer in which cations in the silicate framework are gradually leached out and replaced by protons from the solution. This view has recently been challenged by observations of extremely sharp corrosion fronts and oscillatory zonings in altered rims of the materials, suggesting that corrosion of these materials may proceed directly through congruent dissolution followed by secondary mineral precipitation. Here we show that complex silicate material dissolution behaviors can emerge from a simple positive feedback between dissolution-induced cation release and cation-enhanced dissolution kinetics. This self-accelerating mechanism enables a systematic prediction of the occurrence of sharp dissolution fronts (vs. leached surface layers), oscillatory dissolution behaviors and multiple stages of glass dissolution (in particular the alteration resumption at a late stage of a corrosion process). In conclusion, our work provides a new perspective for predicting long-term silicate weathering rates in actual geochemical systems and developing durable silicate materials for various engineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Controlling Complex Coacervation via Random Polyelectrolyte Sequences

The utilization of chemical sequence control in polymeric materials is key to enabling material design on par with biomacromolecular systems. One important avenue for scalable sequence-controlled polymers leverages the random copolymerization of distinct monomers, with the statistical distribution of the monomeric sequence arising from reaction kinetics following a first-order Markov process. Here we utilize the framework of the random phase approximation (RPA) to develop a theory for the phase behavior of symmetric polyelectrolyte coacervates whose chemical sequences are dictated by simple statistical distributions. We find that a high charge "blockiness" within the random sequences favors the formation of denser and more salt-resistant coacervates while simultaneously increasing the width of the two-phase region. Here, we trace these physical effects to the increased cooperativity of Coulomb interactions that results from increased charge blockiness in oppositely charged polyelectrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Making coordination networks ionic: a unique strategy to achieve solution-processable hybrid semiconductors

The development of high-performance, solution-processable semiconducting materials is crucial for the advancement of emerging clean-energy technologies such as light-emitting diodes and photovoltaics. While hybrid perovskites have shown considerable promise for implementation in these technologies, their reliance on toxic metals and relatively low stability towards moisture and chemical environments remain to be addressed. In this Chemistry Frontiers article, we describe a unique strategy to build nontoxic, robust and solution-processable hybrid semiconductors based on copper halide by incorporating ionic bonds in coordination complexes (molecular or extended network structures). Specifically, these compounds are made of anionic copper(I) halide and cationic organic ligands that form both coordinate and ionic bonds at the inorganic/organic interfaces and are referred to as all-in-one (AIO)-type structures. Here, the unique bonding nature renders the AIO-type structures with greatly enhanced solubility, excellent optical tunability and remarkable framework stability, all highly desirable for thin-film based optoelectronic devices. We will highlight the most recent progress in the development of this material group, including their design strategies, important properties and potential for clean-energy related applications. We will also briefly discuss the existing challenges and future outlook of these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Effect of Thermodynamic and Environmental Factors on Crystallization of DNA‐Origami Superlattices

The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2014

Creating Optimal Fracture Networks

The primary goal of this project is the development of a multi-physics, multi-scale simulation capability to predict the initiation, propagation and maintenance of hydraulically driven fracture networks in heterogeneous geologic materials. The development and validation of integrated code capability, GEOS, that accurately captures the phenomenology of these complex physical processes across a wide range of time- (~10 -6 to ~10 8 s) and length- (~10 -3 to ~10 4 m) scales is the main scientific objective of the proposed initiative. GEOS constitutes a new analytical framework incorporating the underlying physics that drives fracture development – e.g. dynamically changing fracture network topologies, fluid/solid interactions taking place along discrete interfaces, and complex matrix/fracture transport processes. Enhanced seismic observational tools and inversion methods will also be developed. This initiative has positioned LLNL to play a leading role in the development of Enhanced Geothermal Systems, unconventional gas energy resources, risk assessment associated with geologic sequestration of CO 2 and the detection of radioactive gases from clandestine nuclear tests, impacting both energy and national security.

02 PETROLEUM↗

DOE OSTI2022

Benchmarking and Exploring Parameter Space of the 2-Phase Bubble Tracking Model for Liquid Mercury Target Simulation

High intensity proton pulses strike the Spallation Neutron Source (SNS)’s mercury target to provide bright neutron beams. These strikes deposit extensive energy into the mercury and its steel vessel. Prediction of the resultant loading on the target is difficult when helium gas is intentionally injected into the mercury to reduce the loading and to mitigate the pitting damage on the vessel. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. We present a study comparing the measured target strains in the SNS target station with the simulation results of the solid mechanics simulation framework. We investigate a wide range of various physical model parameters, including the number of bubble families, bubble size distribution, viscosity, surface tension, etc. to understand their impact on simulation accuracy. Our initial findings reveal that using 8-10 bubble families in the model renders a simulation strain envelope that covers the experimental ones. Further optimization studies are planned to predict the strain response more accurately.

Lin, Lianshan↗

DOE OSTI2024

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as $J(t)$, from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green–Kubo formulas associated with various transport coefficients, including gradient flows, particle–particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived $J(t)$ and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗

DOE OSTI2022

Unraveling Thermodynamic and Kinetic Contributions to the Stability of Doped Nanocrystalline Alloys using Nanometallic Multilayers

Abstract Targeted doping of grain boundaries is widely pursued as a pathway for combating thermal instabilities in nanocrystalline metals. However, certain dopants predicted to produce grain‐boundary‐segregated nanocrystalline configurations instead form small nanoprecipitates at elevated temperatures that act to kinetically inhibit grain growth. Here, thermodynamic modeling is implemented to select the Mo–Au system for exploring the interplay between thermodynamic and kinetic contributions to nanostructure stability. Using nanoscale multilayers and in situ transmission electron microscopy thermal aging, evolving segregation states and the corresponding phase transitions are mapped with temperature. The microstructure is shown to evolve through a transformation at lower homologous temperatures (<600 °C) where solute atoms cluster and segregate to the grain boundaries, consistent with predictions from thermodynamic models. An increase in temperature to 800 °C is accompanied by coarsening of the grain structure via grain boundary migration but with multiple pinning events uncovered between migrating segments of the grain boundary and local solute clustering. Direct comparison between the thermodynamic predictions and experimental observations of microstructure evolution thus demonstrates a transition from thermodynamically preferred to kinetically inhibited nanocrystalline stability and provides a general framework for decoupling contributions to complex stability transitions while simultaneously targeting a dominant thermal stability regime.

25 ENERGY STORAGE↗

DOE OSTI2018

Water-lubricated intercalation in V2O5·nH2O for high-capacity and high-rate aqueous rechargeable zinc batteries

Co-intercalation is an effective approach to tune the diffusion coefficient with which cations insert into the electrodes for energy storage devices. By applying this concept in aqueous Zn batteries, a prospective system for stationary energy storage, we demonstrate the critical role of structural H2O on Zn2+ intercalation into bilayer V2O5·nH2O. The H2O solvation of Zn2+ largely reduces its effective charge and thus electrostatic interaction with the V2O5 framework, evidently promoting the diffusion of solvated Zn2+. Benefited from the ‘lubricating’ effect, the aqueous Zn battery shows a specific energy of ~ 305 Wh kg-1 (based on cathode), comparable to those of LiCoO2//Graphite batteries. Meanwhile, it can maintain an energy density of 199 Wh kg-1 at a high power density of 24 kW kg-1 with a capacity retention of 88% after 900 cycles, making it a promising candidate for high-performance, low-cost, safe and environmentally friendly energy storage device.

Yan, Mengyu↗

DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Physics of Ultrathin Films and Heterostructures of Rare-Earth Nickelates

The electronic structure of transition metal oxides featuring correlated electrons can be rationalized within the Zaanen-Sawatzky-Allen framework. Following a brief description of the present paradigms of electronic behavior, we focus on the physics of rare-earth nickelates as an archetype of complexity emerging within the charge transfer regime. The intriguing prospect of realizing the physics of high- T c cuprates through heterostructuring resulted in a massive endeavor to epitaxially stabilize these materials in ultrathin form. A plethora of new phenomena unfolded in such artificial structures due to the effect of epitaxial strain, quantum confinement, and interfacial charge transfer. Here we review the present status of artificial rare-earth nickelates in an effort to uncover the interconnection between the electronic and magnetic behavior and the underlying crystal structure. Here, we conclude by discussing future directions to disentangle the puzzle regarding the origin of the metal-insulator transition, the role of oxygen holes, and the true nature of the antiferromagnetic spin configuration in the ultrathin limit.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Calibr8 v.1.0

Calibr8 provides an application to rapidly prototype and perform material model calibration for complex plasticity models using advanced adjoint or forward sensitivity analyses for execution on massively parallel machines. These techniques can be orders of magnitude faster than traditional finite difference approaches for material model calibration. The underlying technology used in Calibr8 is automatic differentiation, which allows for the rapid implementation and testing of new plasticity models within its framework. Additionally, Calibr8 can perform adjoint-based error estimation to approximate discretization errors for user-implemented plasticity models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-10630 O

Granzow, BrianN.↗

DOE OSTI2025

CaptureFOAM

CaptureFOAM provides utilities for fast and accurate direct air capture (DAC) contactor simulations, based on the OpenFOAM framework. At the core, CaptureFOAM is a suite of mass transfer and chemistry models built upon the OpenFOAM thin film model, which allows coarser mesh resolution and less constrictive time step requirements when compared to a traditional volume of fluid approach. This allows CaptureFOAM to conduct device-scale simulations for complex geometries including commercial packing materials and novel contactor shapes, matching experimental results in a wide variety of applications.

Kincaid, Kellis [Oak Ridge National Laboratory (OR↗

NASA NTRS1996

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.

Abdi, Frank↗

DOE OSTI2019

Structural Basis of CO 2 Adsorption in a Flexible Metal-Organic Framework Material

This paper reports on the structural basis of CO 2 adsorption in a representative model of flexible metal-organic framework (MOF) material, Ni(1,2-bis(4-pyridyl)ethylene)[Ni(CN) 4 ] (NiBpene or PICNIC-60). NiBpene exhibits a CO 2 sorption isotherm with characteristic hysteresis and features on the desorption branch that can be associated with discrete structural changes. Various gas adsorption effects on the structure are demonstrated for CO 2 with respect to N 2 , CH 4 and H 2 under static and flowing gas pressure conditions. For this complex material, a combination of crystal structure determination and density functional theory (DFT) is needed to make any real progress in explaining the observed structural transitions during adsorption/desorption. Possible enhancements of CO 2 gas adsorption under supercritical pressure conditions are considered, together with the implications for future exploitation. In situ operando small-angle neutron and X-ray scattering, neutron diffraction and X-ray diffraction under relevant gas pressure and flow conditions are discussed with respect to previous studies, including ex situ, a priori single-crystal X-ray diffraction structure determination. The results show how this flexible MOF material responds structurally during CO 2 adsorption; single or dual gas flow results for structural change remain similar to the static (Sieverts) adsorption case, and supercritical CO 2 adsorption results in enhanced gas uptake. Insights are drawn for this representative flexible MOF with implications for future flexible MOF sorbent design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite

Nuclear graphite is a candidate material for Generation IV nuclear power plants. Porous materials such as graphite can contain complex networks of pores that influence the material's mechanical and irradiation response. A methodology known as the random finite element method (RFEM) was adapted to create synthetic microstructures and predict the influence of porosity on the elastic properties of graphite during oxidation. RFEM combines random field theory and the finite element method in a Monte Carlo framework to estimate the mechanical response of a given grade of graphite. In this research, the random fields were verified through experimental characterization to predict the elastic response of three nuclear graphite grades, ETU-10, IG-110, and 2114. Finite element models (FEM) were generated using segmentations of x-ray computed tomography (XCT) data known as image-based models (IBMs) to validate and compare with the RFEM results and better understand the effects of uniform oxidation in these graphite grades. The RFEM predictions appear to correlate well with the experimental values of the measured Young’s modulus of the three graphite grades and display the same trends as IBMs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. Here, this work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Chen, I-Chi [Ames Laboratory (AMES), Ames, IA (Uni↗