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

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Epitaxial Growth of 2D Core‐Crown SnS 2 /SnSe 2 Heterostructure Through Interfacial Modification with Polyvinylpyrrolidone

Abstract Developing generalized strategies for controlled synthesis of 2D heterostructures remains a significant challenge because the existing approaches often suffer from poor reproducibility and scalability. In this study, a solution synthesis approach for epitaxial core‐crown heterostructures with controlled band alignment, that overcomes these challenges is reported. Polyvinylpyrrolidone (PVP) is used as a structure‐directing agent to reduce lattice mismatch between SnS 2 and SnSe 2 (10‐10) surfaces and direct epitaxial growth of SnSe 2 crown on SnS 2 seed. Additionally, PVP adsorption to the basal plane prevents van der Waals stacking and stabilizes 2D heterostructures during synthesis. Driven by interfacial thermodynamics, the formation of the core‐crown heterostructure is highly reproducible and the size of the 2D heterostructure and relative areas of the core and the crown can be precisely controlled in a two‐step process by varying synthesis times for the seed and the crown. The identified growth pathway for 2D heterostructures can be generalized to other combinations of van der Waals materials to provide a platform for synthesizing micron‐size epitaxial heterostructures with a desired electronic structure for catalysis and microelectronics.

Liu, Lili [Physical and Computational Sciences Dir↗
DOE OSTI2026

HRTEM Imaging and Mechanistic Insights Into Carbon Nanotube Nucleation and Growth on Fe Nanocatalysts in a Thermal Plasma

Thermal plasma decomposition of natural gas is a scalable pathway for the production of hydrogen alongside high-value carbon nanotubes (CNTs). Metals evaporate from an electrode and condense to form seed nanoparticles that nucleate and grow CNTs. However, the lack of mechanistic understanding of the CNT nucleation and growth processes in thermal plasma makes control over CNT diameter, chirality, length, and yield difficult. We debundled and separated CNTs from soots produced using iron (Fe) nanocatalysts, and distributed them on monolayer graphene for high-resolution transmission electron microscopy (HRTEM) imaging to gain mechanistic insights. Full graphene encapsulation was found for relatively small Fe nanoparticles that were molten at high temperatures. Zigzag single-wall or double-wall CNTs (SWCNTs or DWCNTs) appeared to have grown out directly from the graphene covering on the conical or cylindrical bodies of small molten Fe nanodroplets with high curvature. Also, SWCNTs likely grew out from H- or O-atom etched single-wall carbon nanocones observed on conical Fe nanoparticles. A SWCNT/DWCNT could also be generated from the cracked opening of the graphene covering on a face-centered cubic (FCC) Fe nanoparticle. A simple, plausible pathway is proposed for the growth of an open, H-passivated, zigzag SWCNT involving reaction of CH 2 and CH radicals at high temperatures.

Fe nanocatalysts↗
DOE OSTI2025

Friction Self-piercing Riveting Process for 3T Configuration of AA7075-AA7075-AA6022

Friction self-piercing riveting (F-SPR), as a solid-state joining process that generates frictional heat during the process, was utilized to join a 3 T configuration of low-ductility AA7075-AA7075-AA6022 sheets. A multi-step F-SPR process was employed to precisely control the mechanical interlocking and solid-state joining, ensuring a strong joint formation. With control of the plunge depth when the transition from rivet penetration to rivet flaring, two joining conditions were developed to assess mechanical joint integrity: one bearing deep penetration with a smaller interlock, and the other yielding a shallow penetration with a larger interlock. Both strategies met the criteria for a sound rivet joint. Customized lap shear tensile configurations were designed to assess the joint strength at interface between first and second layers, as well as between second and third layers. Both conditions demonstrated great joint strength between the first and second layers, while deeper penetration approach showed stronger solid-state joint, leading to improved joint strength between the second and third layers.

Wang, Tianzhao [ORNL] (ORCID:0000000315337602)↗
DOE OSTI2025

Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at s = 13 TeV with the ATLAS detector

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

Aad, G↗
DOE OSTI2024

Alloying Effects on the Transport Properties of Refractory High-entropy Alloys

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. Here we validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil

This study systematically investigates the enhancement of wear resistance in 3D printed surface textures through both experimental and theoretical approaches. Three distinct surface morphologies (Smooth Surface, Surface with uniformly distributed Pits, and Surface with uniformly distributed Bumps) were fabricated using High-Impact Polystyrene, where the meso-scale textures were precisely controlled through the 3D printing process. Wear behavior was evaluated using a 3-body wear tester in an abrasive particle environment, analyzing the influence of surface textures under various operating conditions. Systematic wear tests revealed that optimally designed surface textures achieved a remarkable 77 % reduction in wear compared to the worst-performing sample. The wear mechanisms were comprehensively characterized through weight loss measurements, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) analyses, elucidating the surface morphology changes and their interaction with wear particles. Notably, the study identified how the geometric characteristics of surface textures influence the movement of wear particles and the distribution of contact stresses. Discrete element method simulations corroborated the experimental findings, providing theoretical validation for the enhanced wear resistance of the optimal structure. The high correlation between simulated wear patterns and experimental results validates the reliability of the proposed design methodology. In conclusion, these results demonstrate that 3D printed surface texturing offers a cost-effective and scalable approach to significantly improve wear resistance in engineering applications, presenting a practical alternative to conventional, high-cost surface engineering methods.

3-body abrasion↗
DOE OSTI2024

Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Covalent Functionalization of Silicon with Plasma-Grown “Fuzzy” Graphene: Robust Aqueous Photoelectrodes for CO 2 Reduction by Molecular Catalysts

Carbon electrodes are ideal for electrochemistry with molecular catalysts, exhibiting facile charge transfer and good stability. Yet for solar-driven catalysis with semiconductor light absorbers, stable semiconductor/carbon interfaces can be difficult to achieve, and carbon’s high optical extinction means it can only be used in ultrathin layers. Here, we demonstrate a plasma-enhanced chemical vapor deposition process that achieves well-controlled deposition of out-of-plane “fuzzy” graphene (FG) on thermally oxidized Si substrates. The resulting Si|FG interfaces possess a silicon oxycarbide (SiOC) interfacial layer, implying covalent bonding between Si and the FG film that is consistent with the mechanical robustness observed from the films. The FG layer is uniform and tunable in thickness and optical transparency by deposition time. Using p-type Si|FG substrates, noncovalent immobilization of cobalt phthalocyanine (CoPc) molecular catalysts was employed for the photoelectrochemical reduction of CO 2 in aqueous solution. The Si|FG|CoPc photocathodes exhibited good catalytic activity, yielding a current density of ∼1 mA/cm 2 , Faradaic efficiency for CO of ∼70% (balance H 2 ), and stable photocurrent for at least 30 h at −1.5 V vs Ag/AgCl under 1-sun illumination. Furthermore, the results suggest that plasma-deposited FG is a robust carbon electrode for molecular catalysts and suitable for further development of aqueous-stable Si photocathodes for CO 2 reduction.

CO2 reduction↗
DOE OSTI2026

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

organic↗
DOE OSTI2024

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

58 GEOSCIENCES↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗
DOE OSTI2024

High-performance cementitious composites containing nanostructured carbon additives made from charred coal fines

Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07 wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06 wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing the proton exchange kinetics of BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3− δ ceramic electrolyte by operando diffuse reflectance infrared Fourier transform spectroscopy

Proton exchange kinetics plays an important role in governing the performance of intermediate-temperature protonic ceramic electrolysis cells (PCECs) for hydrogen production. Our understanding of the nature of the surface hydration reaction at the single-cell level, however, remains very limited, hampering further efficiency improvements. Here, in this study, we developed a custom operando diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) platform that operates under high temperature and steam conditions with applied bias. Quantitative investigations of surface H 2 O/D 2 O isotope exchange in a BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3−δ (BZCYYb1711) protonic electrolyte-based single cell were conducted under different applied voltages using this DRIFTS platform, to gain molecular-level insight into hydration kinetics. The findings show that the application of an external voltage significantly enhances the surface proton exchange rate, decreasing the apparent activation energy from 29.1 kJ mol −1 at open-circuit voltage (OCV) to 6.8 kJ mol −1 at 1.3 V. In addition, distinct voltage-induced spectral shifts in O–D vibrations point to dynamic changes in surface hydration. These findings demonstrate a sensitive spectroscopic platform for probing interfacial proton processes and reveal strong electrochemical control over surface proton kinetics, offering new opportunities for probing electrolyte hydration behavior in PCECs.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

PigmentHunter: A point-and-click application for automated chlorophyll-protein simulations

Chlorophyll proteins (CPs) are the workhorses of biological photosynthesis, working together to absorb solar energy, transfer it to chemically active reaction centers, and control the charge-separation process that drives its storage as chemical energy. Yet predicting CP optical and electronic properties remains a serious challenge, driven by the computational difficulty of treating large, electronically coupled molecular pigments embedded in a dynamically structured protein environment. To address this challenge, we introduce here an analysis tool called PigmentHunter, which automates the process of preparing CP structures for molecular dynamics (MD), running short MD simulations on the nanoHUB.org science gateway, and then using electrostatic and steric analysis routines to predict optical absorption, fluorescence, and circular dichroism spectra within a Frenkel exciton model. Inter-pigment couplings are evaluated using point-dipole or transition-charge coupling models, while site energies can be estimated using both electrostatic and ring-deformation approaches. The package is built in a Jupyter Notebook environment, with a point-and-click interface that can be used either to manually prepare individual structures or to batch-process many structures at once. Here, we illustrate PigmentHunter’s capabilities with example simulations on spectral line shapes in the light harvesting 2 complex, site energies in the Fenna–Matthews–Olson protein, and ring deformation in photosystems I and II.

14 SOLAR ENERGY↗
DOE OSTI2025

Rad-hard readout system for Timepix3 Hybrid Pixel Detectors

The Beam Gas Ionisation (BGI) profile monitor, located in the Proton Synchrotron (PS) and Super Proton Synchrotron (SPS) at CERN, requires a radiation-tolerant readout system to transfer data from the challenging accelerator surroundings to the back-end for processing. The system needs to control and acquire data from four Timepix3 Hybrid Pixel Detectors (HPDs) located directly inside the beam pipe, a highly radioactive environment. It must ensure reliability given limited hardware access and preserve signal integrity for the high-speed data (32 channels at 320 MHz). However, due to the unavailability of a suitable rad-hard Timepix3 readout, the Beam Instrumentation PiXeL (BIPXL) readout system was designed to meet these requirements. This system employs radiation-hardened components such as the GBTx and the FEASTMP, both developed at CERN. It will be compatible with forthcoming hybrid pixel detector initiatives in similarly harsh radiation conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Deep Learning Approach for In-Network Synchrophasor Missing Data Recovery Using Programmable Network Switches

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today’s electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

Phasor Measurement Unit, Machine Learning, Program↗
DOE OSTI2025

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.

36 MATERIALS SCIENCE↗