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DOE OSTI2021

Broadband Circular Polarizers via Coupling in 3D Plasmonic Meta-Atom Arrays

In this work, we report broadband circular polarizers achieved by engineering the electromagnetic coupling between 3D meta-atoms in large-area arrays. The 3D meta-atoms are composed of bulk Au/Au nanocrystal (NC) bilayer helical arms, tailored in their number, length, and curvature through a one-step patterning process and postfabrication chemical and thermal treatments. By tuning the meta-atom array periodicity, hybridization originating from dipole–dipole and quadrupole–quadrupole interactions broadens the chiral response. We demonstrate circular polarizers operating at wavelengths spanning 2.5 to 5 μm, with a maximal transmission difference between left- and right-hand circularly polarized light of 43%.

3D metamaterials↗

DOE OSTI2024

Influence of temperature, doping, and amorphization on the electronic structure and magnetic damping of iron

Hybrid magnonic quantum systems have drawn increased attention in recent years for coherent quantum information processing, but too large magnetic damping is a persistent concern when metallic magnets are used. Their intrinsic damping is largely determined by electron-magnon scattering induced by spin-orbit interactions. In the low scattering limit, damping is dominated by intra-band electronic transitions, which has been theoretically shown to be proportional to the electronic density of states at the Fermi level. In this work, we focus on body-centered-cubic iron as a paradigmatic ferromagnetic material. We comprehensively study its electronic structure using first-principles density functional theory simulations and account for finite lattice temperature, boron (B) doping, and structure amorphization. Our results indicate that temperature induced atomic disorder and amorphous atomic geometries only have a minor influence. Instead, boron doping noticeably decreases the density of states near the Fermi level with an optimal doping level of 6.25%. In addition, we show that this reduction varies significantly for different atomic geometries and report that the highest reduction correlates with a large magnetization of the material. Furthermore, this may suggest materials growth under external magnetic fields as a route to explore in experiment.

36 MATERIALS SCIENCE↗

DOE OSTI2023

2022 American Conference on Neutron Scattering (ACNS 2022)

The 11th American Conference on Neutron Scattering (ACNS 2022) will be held on June 5-9, 2022, in Boulder, CO. The Conference will provide essential information on the breadth and depth of current neutron-related research worldwide. Hosted by the Neutron Scattering Society of America, this year’s Conference will feature a combination of invited and contributed talks, poster sessions, and tutorials. Topics of the conference are: Advances in Neutron Facilities, Instrumentation and Software: Developments in sources, instrumentation, sample environments and control software. Hard Condensed Matter: Magnetism, correlated metals, quantum/topological materials, superconductors, ferroelectrics, multiferroics, glasses, and disorder phenomena. Submissions outlining examples of neutron scattering in industrial and engineering applications involving hard condensed matter systems are also encouraged. Soft Matter: Neutron studies of soft materials and related fields including in situ and in operando studies. Polymers, surfactants, emulsions, gels, nanoparticles, colloidal suspensions and more. Submissions of computational studies or applications of machine learning beneficial to neutron scattering experiments, as well as examples of neutron scattering in industrial and engineering applications are strongly encouraged. Biology, Biophysics and Biotechnology: Neutron studies of biological and biologically relevant systems. Proteins, bio membranes, biological assemblies, natural materials, nucleic acids, drug-delivery platforms and biomedical systems. Submissions of computational studies or applications of machine learning beneficial to biological neutron scattering experiments, as well as examples of neutron scattering in applied research involving biological systems, are strongly encouraged. Materials Chemistry and Energy: Neutron-based studies of functional materials and materials for energy applications. Examples include porous materials such as metal organic frameworks (MOFs), zeolites; phosphors; novel pigments; electrolytes; catalysts; ionic conductors/cathode materials; photovoltaic materials (hybrid perovskites); thermoelectrics; magnetocalorics/electrocalorics. Structural Materials and Engineering: Neutron scattering studies of materials and engineering processes including structural materials, concrete and metals, as well as engineering processes including combustion, corrosion, additive manufacturing, and others. Neutron Physics: Fundamental physical studies of the neutron and related areas. Emerging Applications in Neutron Scattering: Machine Learning and Data Science: Advances in computing power have contributed to rapidly evolving machine learning and data science fields that can be leveraged to the benefit of the neutron scattering community. The purpose of this session is to highlight recent advances in machine learning and data science and to serve as the foundation of a parallel data and computation track highlighting computation advances and applications in neutron scattering throughout the conference.

36 MATERIALS SCIENCE↗

NASA NTRS2002

COI NMSD Hybrid Mirror

This presentation provides an overview of the development of the 1.6 meter hybrid mirror demonstrator for the NGST Mirror System Demonstrator (NMSD) program. The COI design approach for the NGST program combines the optical performance of glass, with the high specific stiffness capabilities of composite materials The foundation technologies being exploited in the development of the hybrid mirror focus upon precision Composite Materials for cryogenic operation, and non-contact optical processing (ion figuring) of the lightweight mirror surface. The NGST Mirror System Demonstrator (NMSD) has been designed and built by Composite Optics, Inc. (COI) with optical processing performed by SAGEM (REOSC). The sponsors of these efforts are the NASA Marshall and Goddard Space Flight Centers.

Mehle, Greg↗

NASA NTRS2005

Emissions Prediction and Measurement for Liquid-Fueled TVC Combustor with and without Water Injection

An investigation is performed to evaluate the performance of a computational fluid dynamics (CFD) tool for the prediction of the reacting flow in a liquid-fueled combustor that uses water injection for control of pollutant emissions. The experiment consists of a multisector, liquid-fueled combustor rig operated at different inlet pressures and temperatures, and over a range of fuel/air and water/fuel ratios. Fuel can be injected directly into the main combustion airstream and into the cavities. Test rig performance is characterized by combustor exit quantities such as temperature and emissions measurements using rakes and overall pressure drop from upstream plenum to combustor exit. Visualization of the flame is performed using gray scale and color still photographs and high-frame-rate videos. CFD simulations are performed utilizing a methodology that includes computer-aided design (CAD) solid modeling of the geometry, parallel processing over networked computers, and graphical and quantitative post-processing. Physical models include liquid fuel droplet dynamics and evaporation, with combustion modeled using a hybrid finite-rate chemistry model developed for Jet-A fuel. CFD and experimental results are compared for cases with cavity-only fueling, while numerical studies of cavity and main fueling was also performed. Predicted and measured trends in combustor exit temperature, CO and NOx are in general agreement at the different water/fuel loading rates, although quantitative differences exist between the predictions and measurements.

Brankovic, A.↗

NASA NTRS2007

Fabrication of Polyimide-Matrix/Carbon and Boron-Fiber Tape

The term HYCARB denotes a hybrid composite of polyimide matrices reinforced with carbon and boron fibers. HYCARB and an improved process for fabricating dry HYCARB tapes have been invented in a continuing effort to develop lightweight, strong composite materials for aerospace vehicles. Like other composite tapes in this line of development, HYCARB tapes are intended to be used to build up laminated structures having possibly complex shapes by means of automated tow placement (ATP) - a process in which a computer-controlled multiaxis machine lays down prepreg tape or tows. The special significance of the present process for making dry HYCARB for ATP is that it contributes to the reduction of the overall cost of manufacturing boron-reinforced composite-material structures while making it possible to realize increased compression strengths. The present process for making HYCARB tapes incorporates a "wet to dry" process developed previously at Langley Research Center. In the "wet to dry" process, a flattened bundle of carbon fiber tows, pulled along a continuous production line between pairs of rollers, is impregnated with a solution of a poly(amide acid) in N-methyl-2-pyrrolidinone (NMP), then most of the NMP is removed by evaporation in hot air. In the present case, the polyamide acid is, more specifically, that of LaRC. IAX (or equivalent) thermoplastic polyimide, and the fibers are, more specifically, Manganite IM7 (or equivalent) polyacrylonitrile- based carbon filaments that have a diameter of 5.2 m and are supplied in 12,000-filament tows. The present process stands in contrast to a prior process in which HYCARB tape was made by pressing boron fibers into the face of a wet carbon-fiber/ poly(amide acid) prepreg tape . that is, a prepreg tape from which the NMP solvent had not been removed. In the present process, one or more layer(s) of side-by-side boron fibers are pressed between dry prepreg tapes that have been prepared by the aforementioned gwet to dry h process. The multilayer tape is then heated to imidize the matrix material and remove most of the remaining solvent, and is pressed to consolidate the multiple layers into a dense tape. For tests, specimens of HYCARB tapes and laminated composite panels made from HYCARB tape were prepared as follows: HYCARB tapes were fabricated as described above. Each panel was made by laying down ten layers of tape, containing, variously, one, two, or three boron-fiber plies and the remainder carbon- fiber-only plies (see figure). Each panel was made by laying down ten layers of tape. Each panel was then cured by heating to a temperature of 225 C for 15 minutes, then pressing at 200 psi (A1.4 MPa) while heating to 371 C, holding at 371 C for 1 hour, then continuing to hold pressure during cooling. Control specimens that were otherwise identical except that they did not contain boron fibers also were prepared. In room-temperature flexural tests, the HYCARB specimens performed comparably to the control specimens; in room-temperature, open-hole compression tests, the HYCARB specimens performed slightly better, by amounts that increased with boron content.

Belvin, Harry L.↗

DOE OSTI2020

Hybrid nanocapsules for in situ TEM imaging of gas evolution reactions in confined liquids

Liquid cell transmission electron microscopy (TEM) enables the direct observation of dynamic physical and chemical processes in liquids at the nanoscale. Quantitative investigations into reactions with fast kinetics and/or multiple reagents will benefit from further advances in liquid cell design that facilitate rapid in situ mixing and precise control over reagent volumes and concentrations. This work reports the development of inorganic–organic nanocapsules for high-resolution TEM imaging of nanoscale reactions in liquids with well-defined zeptoliter volumes. These hybrid nanocapsules, with 48 nm average diameter, consist of a thin layer of gold coating a lipid vesicle. As a model reaction, the nucleation, growth, and diffusion of nanobubbles generated by the radiolysis of water is investigated inside the nanocapsules. When the nanobubbles are sufficiently small (10–25 nm diameter), they are mobile in the nanocapsules, but their movement deviates from Brownian motion, which may result from geometric confinement by the nanocapsules. Gases and fluids can be transported between two nanocapsules when they fuse, demonstrating in situ mixing without using complex microfluidic schemes. The ability to synthesize nanocapsules with controlled sizes and to monitor dynamics simultaneously inside multiple nanocapsules provides opportunities to investigate nanoscale processes such as single nanoparticle synthesis in confined volumes and biological processes such as biomineralization and membrane dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Solid-Binding Proteins: Bridging Synthesis, Assembly, and Function in Hybrid and Hierarchical Materials Fabrication

There is considerable interest in the development of hybrid organic–inorganic materials because of the potential for harvesting the unique capabilities that each system has to offer. Proteins are an especially attractive organic component owing to the high amount of chemical information encoded in their amino acid sequence, their amenability to molecular and computational (re)design, and the many structures and functions they specify. Genetic installation of solid-binding peptides (SBPs) within protein frameworks affords control over the position and orientation of adhesive and morphogenetic segments, and a path toward predictive synthesis and assembly of functional materials and devices, all while harnessing the built-in properties of the host scaffold. Furthermore, we review the current understanding of the mechanisms through which SBPs bind to technologically relevant interfaces, with an emphasis on the variables that influence the process, and highlight the last decade of progress in the use of solid-binding proteins for hybrid and hierarchical materials synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Inverse Text Normalization of Air Traffic Control System Command Center Planning Telecon Transcriptions

We present a hybrid neural network and rule-based Inverse Text Normalization (ITN) method for domains containing unique technical phraseology, specifically ATCSCC planning telecon audio transcriptions. The Air Traffic Control System Command Center (ATCSCC) hosts bihourly planning telephone conferences (or planning telecons) to ensure smooth operations within the National Airspace (NAS). Access to both live and post meeting transcripts of this speech audio would enable quick review of meetings. ITN is the process of converting un-formatted "raw'' speech-to-text transcripts into a human (expert) readable written form. Our hybrid ITN framework utilizes a neural network to format conversational English, and rule-based methods to format domain-specific aviation text. With a preliminary overall Word Error Rate with Punctuation and Capitalization (WER PC) of 8.26, we show that this method has vast potential in being applied to ATCSCC planning telecon audio and other audio/text based data available in ATM.

Air Traffic Management↗

NASA NTRS1997

Physics of Boundaries and their Interactions in Space Plasmas

This report describes the work done by SciberNet, Inc. during the month of October. We are working on the further refinement of the model used in our large-scale hybrid simulations of the magnetopause. Specifically, we are experimenting with several ways of modeling the effects of cold magnetospheric ions into our simulations. In addition, we are preparing two presentations for the upcoming Fall AGU highlighting the results of these simulations. We have also made progress in our development of a new kinetic linear code which we are using to study the linear properties of the Kelvin-Helmholtz instability at the magnetopause. We have extended the code from the electrostatic limit to the fully electromagnetic regime and are currently in the process of debugging and testing the code. Finally, we have made several test runs with our 2-D hybrid code for the magnetopause. The inflow-outflow boundary conditions are working properly. However, there are issues related to the setup and evolution of the original equilibrium that we are still trying to resolve. Finally, we are preparing several presentations for the upcoming Fall AGU.

Omidi, Nojan↗

NASA NTRS2001

Organic-Inorganic Hybrids Using Novel Phenylethynyl Imide Silanes

In this presentation, polyimide-silica hybrids using novel phenylethynyl imide silanes are reported. The phenylethynyl group is present in the organic precursor as either a pendent or an end group to bond chemically with the polyimide adhesive containing phenylethynyl groups during processing, while the silane group of the organic precursor would chemically react with the inorganic precursor through oxane bond formation. The chemical compositions of these novel hybrids were examined using X-ray mapping modes of scanning electron microscopy (SEM), which revealed a silicon gradient interphase between the high surface energy substrate and the polyimide adhesive. Novel aromatic phenylethynyl imide silanes (APEISs) and pendent phenylethynyl imide oligomeric disilanes (PPEIDSs) have been synthesized, and sol-gel solutions containing the new silanes, a phenylethynyl terminated imide oligomer (PETI-5), and an inorganic precursor were formulated to develop a gradient hybrid interphase between a titanium alloy and the adhesive. Two different sol-gel systems were investigated to develop organic-inorganic hybrids. Hybrid I was composed of an organic precursor containing both phenylethynyl and silane groups (PPEIDS) and an inorganic precursor. Functional group concentrations were controlled by the variation of the molecular weight of the imide backbone of PPEIDS. Hybrid II was composed of organic and inorganic precursors and a coupling agent containing both phenylethynyl and silane groups. Morphology and chemical composition of the hybrid interphase between the inorganic substrate and the adhesive were investigated, and the bond strength and durability were evaluated using lap shear tests at various conditions. The assessment of how the bonding at an interface is affected by various sol-gel solution compositions and environments is reported.

Park, C.↗

NASA NTRS1996

4482 Element Multispectral Hybrid PV/PC HgCdTe IRFPA for High Resolution Coverage of 3.7 - 15.4 Micrometers for the AIRS Instrument

The Atmospheric Infrared Sounder (AIRS) is a key facility instrument in the NASA Earth Observing System (EOS) program, being implemented to obtain comprehensive long-term measurements of earth processes affecting global change. The instrument performs passive IR remote sensing using a high resolution grating spectrometer with a wide spectral coverage (3.7 - 15.4 m) directing radiation onto a hybrid HgCdTe IRFPA operating at 58K in a vacuum package cooled to 155K. The hybrid HgCdTe FPA consists of twelve modules, 10 with multiplexed photovoltaic detectors and two with individually leaded out photoconductive detectors. The complex FPA has a large optical footprint, 53 mm x 66 mm, and receives energy dispersed from the grating through a precision filter assembly containing 17 narrow band filters. The backside illuminated PV detector arrays are fabricated from P-on-n double layer LPE grown heterojunction detectors in a bilinear format of 50 m x 100 m detectors, with from 232 to 420 detectors per module. For the MWIR bands four PV modules cover the 3.7 m to 8.22 m region. Low detector capacitance and low noise preamplifiers in the ROIC are key to achieving high sensitivities in these bands. Uniform quantum efficiencies and detectivities exceeding 3E13 cm-rtHz/W have been achieved. The LWIR region is covered by six PV modules spanning 8.8 m to 13.75 m. High detector resistance and very low ROIC preamplifier input noise are key to achieving high sensitivity. A detectivity exceeding 2E11 cm-rtHz/W has been achieved at the longest wavelength. Two additional PC modules cover the longest spectral bands out to 15.4 m. This high performance multispectral focal plane has been built and integrated with the dewar assembly, and is currently being integrated with the complete AIRS sensor.

Rutter, James↗

DOE OSTI2022

Evolution of Programmatic Asset Lifecycle Planning at MESA

Early on in 2018 Sandia recognized the Microsystems Engineering, Science and Applications (MESA) Programmatic Asset Lifecycle Planning capability to be unpredictable, inconsistent, reactive, and unable to provide strong linkage to the sponsor's needs. The impetus for this report is to share learnings from MESA's journey towards maturing this capability. This report describes re-building the foundational elements of MESA's Programmatic Asset Lifecycle Planning capability using a risk-based, Multi-Criteria Decision Analysis (MCDA) approach. To begin, MESA's decades-old Piano Chart + Ad Hoc Hybrid Methodology is described with a narrative of its strengths and weaknesses. Then its replacement, the MCDA /Analytical Hierarchy Process, is introduced with a discussion of its strengths and weaknesses. To generate a realistic Programmatic Asset Lifecycle Planning budget outlook, MESA used its rolling 20-year Extended Life Program Plan (MELPP) as a baseline. The new MCDA risk-based prioritization methodology implements DOE/NNSA guidelines for prioritization of DOE activities and provides a reliable, structured framework for combining expert judgement and stakeholder preferences according to an established scientific technique. An in-house Hybrid Decision Support System (HDSS) software application was developed to facilitate production of several key deliverables. The application enables analysis of the prioritization decisions with charts to display and provide linkage of MESA's funding requests to the stakeholders' priorities, strategic objectives, nuclear deterrence programs, MESA priorities, and much more.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Inverse Text Normalization of Air Traffic Control System Command Center Planning Telecon Transcriptions

We present a hybrid neural network and rule-based Inverse Text Normalization (ITN) method for domains containing unique technical phraseology, specifically Air Traffic Control System Command Center (ATCSCC) planning telecon audio transcriptions. The ATCSCC hosts bi-hourly planning telephone conferences (or planning telecons) to ensure smooth operations within the National Airspace (NAS). Access to both live and post meeting transcripts of this speech audio would enable quick review of meetings. Provided speech transcripts, ITN is the process of converting unformatted raw Automated Speaker Recognition (ASR) model transcripts into a human (expert) readable written form. Our hybrid ITN framework utilizes a fine-tuned Bidirectional Encoder Representations from Transformers neural network to format conversational English, and rule-based methods to format domain-specific aviation text. With an overall Punctuation Error Rate (PER) of 25.56 and Word Error Rate with Punctuation and Capitalization (WER PC) of 5.47, we show that this method has vast potential in being applied to ATCSCC planning telecon audio and other audio/text based data available in ATM.

ATM↗

NASA NTRSDate not supplied

Inverse Text Normalization of Air Traffic Control System Command Center Planning Telecon Transcriptions

ATM↗

DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO 2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO 2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO 2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NN C-part ). NN C-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R 2 > .94) and RECO (R 2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO 2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO 2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN C-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗