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DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

The accumulation and extrusion of Ca 2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. In order to emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca 2+ , respectively. Furthermore, the diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

36 MATERIALS SCIENCE↗

DOE OSTI2018

A browser-based tool for visualization and analysis of diffusion MRI data

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Enhancing carbon dioxide gas-diffusion electrolysis by creating a hydrophobic catalyst microenvironment

Electroreduction of carbon dioxide (CO 2 ) over copper-based catalysts provides an attractive approach for sustainable fuel production. While efforts are focused on developing catalytic materials, it is also critical to understand and control the microenvironment around catalytic sites, which can mediate the transport of reaction species and influence reaction pathways. Here, we show that a hydrophobic microenvironment can significantly enhance CO 2 gas-diffusion electrolysis. For proof-of-concept, we use commercial copper nanoparticles and disperse hydrophobic polytetrafluoroethylene (PTFE) nanoparticles inside the catalyst layer. Consequently, the PTFE-added electrode achieves a greatly improved activity and Faradaic efficiency for CO 2 reduction, with a partial current density >250 mA cm -2 and a single-pass conversion of 14% at moderate potentials, which are around twice that of a regular electrode without added PTFE. The improvement is attributed to a balanced gas/liquid microenvironment that reduces the diffusion layer thickness, accelerates CO 2 mass transport, and increases CO 2 local concentration for the electrolysis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Pseudo-bilayer architecture enables high-performance organic solar cells with enhanced exciton diffusion length

Solution-processed organic solar cells (OSCs) are a promising candidate for next-generation photovoltaic technologies. However, the short exciton diffusion length of the bulk heterojunction active layer in OSCs strongly hampers the full potential to be realized in these bulk heterojunction OSCs. Herein, we report high-performance OSCs with a pseudo-bilayer architecture, which possesses longer exciton diffusion length benefited from higher film crystallinity. This feature ensures the synergistic advantages of efficient exciton dissociation and charge transport in OSCs with pseudo-bilayer architecture, enabling a higher power conversion efficiency (17.42%) to be achieved compared to those with bulk heterojunction architecture (16.44%) due to higher short-circuit current density and fill factor. A certified efficiency of 16.31% is also achieved for the ternary OSC with a pseudo-bilayer active layer. Our results demonstrate the excellent potential for pseudo-bilayer architecture to be used for future OSC applications.

14 SOLAR ENERGY↗

DOE OSTI2023

Probing the atomically diffuse interfaces in Pd@Pt core-shell nanoparticles in three dimensions

Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is found to be atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle’s morphology or crystallographic texture. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds, which is confirmed by atomic images of Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Charge decay in the spatial afterglow of plasmas and its impact on diffusion regimes

Abstract The spatial afterglow is a region at the boundary of a non-equilibrium plasma where charged species relax into ambient equilibrium. In many applications, the spatial afterglow is the part of the plasma that interacts with surfaces, such as suspended particles or a material substrate. However, compared to the bulk plasma, there has been little effort devoted to studying the properties of the spatial afterglow, and a fundamental analysis has not yet been developed. Here, we apply double Langmuir probe measurements and develop an advection-diffusion-recombination model to provide a detailed description of charged species in the spatial afterglow over a wide range of pressures, temperatures, plasma dimensions, and flow rates. We find that the density of charged species in the spatial afterglow decays by orders of magnitude, which leads to a transition from ambipolar to free diffusion. These insights can be used to explain or predict experimental observations of phenomena, such as the charging of dust grains and the dose of charged species to a biomaterial.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Diffusion-free Grotthuss topochemistry for high-rate and long-life proton batteries

The design of Faradaic battery electrodes that exhibit high rate capability and long cycle life equivalent to those of the electrodes of electrical double-layer capacitors is a big challenge. Here we report a strategy to fill this performance gap using the concept of Grotthuss proton conduction, in which proton transfer takes place by means of concerted cleavage and formation of O-H bonds in a hydrogen-bonding network. We show that in a hydrated Prussian blue analogue (Turnbull's blue) the abundant lattice water molecules with a contiguous hydrogen-bonding network facilitate Grotthuss proton conduction during redox reactions. When using it as a battery electrode, we find high-rate behaviours at 4,000 C (380 Ag -1 , 508 mA cm -2 ), and a long cycling life of 0.73 million cycles. Furthermore these results for diffusion-free Grotthuss topochemistry of protons, in contrast to orthodox battery electrochemistry, which requires ion diffusion inside electrodes, indicate a potential direction to revolutionize electrochemical energy storage for high-power applications.

25 ENERGY STORAGE↗

DOE OSTI2021

Atomistic observation on diffusion-mediated friction between single-asperity contacts

Super-lubricity, a sliding regime in which friction almost vanishes, has been well-documented with the advent of atomic force microscopy (AFM) and molecular dynamics (MD) simulation due to its enormous implications for manufacturing micro-/nano-electromechanical systems (MEMS/NEMS). Nevertheless, the atomistic mechanisms for super-lubricious behaviors are still elusive, primarily due to a lack of direct observation of interface at atomic resolution during frictional process. Here by using in situ high-resolution transmission electron microscopy (HRTEM) coupled with AFM, we report diffusion-mediated formation of a loosely-packed interfacial layer between two metallic asperities at infinitesimal normal forces, which is responsible for the sliding regime, super-lubricity exhibiting ultra-low friction forces and continuous sliding. By contrast, the loosely-packed interfacial layer vanishes along with the super-lubricity, leading to typical stick-slip friction. Additionally, in situ TEM observation and MD simulations reveals the crucial role that atom diffusion plays in atomic friction, and provides new insights into the fundamental mechanisms of super-lubricity.

42 ENGINEERING↗

DOE OSTI2017

Water sub-diffusion in membranes for fuel cells

We investigate the dynamics of water confined in soft ionic nano-assemblies, an issue critical for a general understanding of the multi-scale structure-function interplay in advanced materials. We focus in particular on hydrated perfluoro-sulfonic acid compounds employed as electrolytes in fuel cells. These materials form phase-separated morphologies that show outstanding proton-conducting properties, directly related to the state and dynamics of the absorbed water. We have quantified water motion and ion transport by combining Quasi Elastic Neutron Scattering, Pulsed Field Gradient Nuclear Magnetic Resonance, and Molecular Dynamics computer simulation. Effective water and ion diffusion coefficients have been determined together with their variation upon hydration at the relevant atomic, nanoscopic and macroscopic scales, providing a complete picture of transport. We demonstrate that confinement at the nanoscale and direct interaction with the charged interfaces produce anomalous sub-diffusion, due to a heterogeneous space-dependent dynamics within the ionic nanochannels. This is irrespective of the details of the chemistry of the hydrophobic confining matrix, confirming the statistical significance of our conclusions. Our findings turn out to indicate interesting connections and possibilities of crossfertilization with other domains, including biophysics. They also establish fruitful correspondences with advanced topics in statistical mechanics, resulting in new possibilities for the analysis of Neutron scattering data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Nonlinear model of infection wavy oscillation of COVID-19 in Japan based on diffusion kinetics

The infectious propagation of SARS-CoV-2 is continuing worldwide, and specifically, Japan is facing severe circumstances. Medical resource maintenance and action limitations remain the central measures. An analysis of long-term follow-up reports in Japan shows that the infection number follows a unique wavy oscillation, increasing and decreasing over time. However, only a few studies explain the infection wavy oscillation. This study introduces a novel nonlinear mathematical model of the new infection wavy oscillation by applying the macromolecule diffusion theory. In this model, the diffusion coefficient that depends on population density gives nonlinearity in infection propagation. As a result, our model accurately simulated infection wavy oscillations, and the infection wavy oscillation frequency and amplitude were closely linked with the recovery rate of infected individuals. In conclusion, our model provides a novel nonlinear contact infection analysis framework.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Impact of intermediate sites on bulk diffusion barriers: Mg intercalation in Mg 2 Mo 3 O 8

The ongoing search for high voltage positive electrode materials for Mg batteries has been primarily hampered by poor Mg mobility in bulk oxide frameworks. Thus motivated by the presence of Mo 3 clusters that can facilitate charge redistribution and the presence of Mg in a non-preferred (tetrahedral) coordination environment, we have investigated the Mg (de)intercalation behavior in layered-Mg 2 Mo 3 O 8 , a potential positive electrode. While no electrochemical activity is observed, chemical demagnesiation of Mg 2 Mo 3 O 8 is successful but leads to amorphization. Subsequent first-principles calculations predict a strong thermodynamic driving force for structure decomposition at low Mg concentrations and high activation barriers for bulk Mg diffusion, in agreement with experimental observations. Further analysis of the Mg diffusion pathway reveals an O–Mg–O dumbbell intermediate site that creates a high Mg 2+ migration barrier, indicating the influence of transition states on setting the magnitude of migration barriers.

25 ENERGY STORAGE↗

DOE OSTI2018

Modeling gas-diffusion electrodes for CO 2 reduction

CO 2 reduction conducted in electrochemical cells with planar electrodes immersed in an aqueous electrolyte is severely limited by mass transport across the hydrodynamic boundary layer. This limitation can be minimized by use of vapor-fed, gas-diffusion electrodes (GDEs), enabling current densities that are almost two orders of magnitude greater at the same applied cathode overpotential than what is achievable with planar electrodes in an aqueous electrolyte. The addition of porous cathode layers, however, introduces a number of parameters that need to be tuned in order to optimize the performance of the GDE cell. In this study, we develop a multiphysics model for gas diffusion electrodes for CO 2 reduction and used it to investigate the interplay between species transport and electrochemical reaction kinetics. The model demonstrates how the local environment near the catalyst layer, which is a function of the operating conditions, affects cell performance. Finally, we also examine the effects of catalyst layer hydrophobicity, loading, porosity, and electrolyte flowrate to help guide experimental design of vapor-fed CO 2 reduction cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Emerging investigator series: ion diffusivities in nanoconfined interfacial water films contribute to mineral carbonation thresholds

The dynamics and reactivity of nanoconfined fluids play critical roles across a wide range of environmental and technological systems, though reaction mechanisms and kinetics are not well understood. Here, the carbonation kinetics of forsterite (Mg 2 SiO 4 ) exposed to 90 atm supercritical carbon dioxide at 35–65 °C and 85–100% relative humidity (RH) was monitored with in situ X-ray diffraction, and partner molecular dynamics simulations were used to describe the free energy landscape of Mg 2+ adsorption and diffusion on forsterite surfaces covered in water films 3–10 monolayers thick. The collective findings reveal how decreasing the water film thickness by ~1.4 monolayers, from ~0.92 to ~0.64 nm, inhibited reaction rates by up to 97%, promoted anhydrous Mg-carbonate (magnesite, MgCO 3 ) precipitation, and more than doubled the apparent activation energy of carbonation. The transport simulations suggest that four monolayers are required to enable sufficiently facile Mg 2+ diffusion, helping explain previously observed water film thickness-dependent reactivity thresholds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Molecular interactions in diffusion-controlled aldol condensation with mesoporous silica nanoparticles

The aldol reaction of p-nitrobenzaldehyde in amino-catalyzed mesoporous silica nanoparticles (MSN) has revealed varying catalytic activity with the size of the pores of MSN. The pore size dependence related to the reactivity indicates that the diffusion process is important. A detailed molecular-level analysis for understanding diffusion requires assessment of the noncovalent interactions of the molecular species involved in the aldol reaction with each other, with the solvent, and with key functional groups on the pore surface. Such an analysis is presented here based upon the effective fragment potential (EFP). The EFP method can calculate the intermolecular interactions, decomposed into Coulomb, polarization, dispersion, exchange-repulsion, and charge-transfer interactions. In this study, the potential energy surfaces corresponding to each intermolecular interaction are analyzed for homo- and hetero-dimers with various configurations. The monomers that compose dimers are five molecules such as p-nitrobenzaldehyde, acetone, n-hexane, propylamine, and silanol. The results illustrate that the dispersion interaction is crucial in most dimers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗