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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum mechanical model of crossing and anti-crossing points in 3D full-band Monte Carlo simulations

This work presents a 3D quantum mechanics based model to address the physics at band structure crossing/anti-crossing points in full band Monte Carlo (FBMC) simulations. The model solves the Krieger and Iafrate (KI) equations in real time using pre-computed coefficients at k-points spatially sampled within the first Brillouin zone. Solving the KI equations in real time makes this model applicable for all electric fields, which enables its use in FBMC device simulations. In this work, a two-level refinement scheme is used to aggressively sample regions in proximity to band crossings for accurate solutions to the KI equations and coarsely sample everywhere else to limit the number of k-points used. The presented sampling method is demonstrated on the band structure of silicon but is effective for the band structure of any semiconductor material. Next, the adaptation of the fully quantum KI model into the semi-classical FBMC method is discussed. Finally, FBMC simulations of hole transport in 4H silicon carbide with and without the KI model are performed. Results along different crystallographic directions for a wide range of electric fields are compared to previously published simulation and experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Nanoscale mapping of point defect concentrations with $\mathrm{4D-STEM}$

Vacancies are missing atoms in a crystalline material, and occur both at equilibrium (varying with temperature) and out of equilibrium such as when crystalline materials are damaged with radiation or corrosion. While we know of their importance, particularly regarding diffusive mechanisms, it is not straightforward to experimentally measure their concentration or to visualize them directly. Traditionally, measurements such as positron annihilation spectroscopy and to some degree X-ray diffraction can measure average concentrations, but generally lack the ability to visualize or quantify a heterogenous concentration of vacancies that can occur at the level of individual defects and microstructural features. Here, we present a method to map vacancy concentrations and their distribution using local lattice parameter measurements with a high-resolution electron microscope. Our method utilizes a Au thin film as a model to demonstrate the method via four-dimensional scanning transmission electron microscopy (4D-STEM) by correlating the differences between changes in lattice parameter and the volumetric thermal expansion during in situ heating experiments. The vacancy mapping methodology is also applied to non-equilibrium defects accumulated in pure Al via knock-on electron beam irradiation. Our method demonstrates the ability to map point defect concentrations in heterogeneous systems in situ with nanometer spatial resolution. The result is a technique that can provide direct measurements of vacancy concentrations at the level of individual defects in studies of materials in and out of equilibrium.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Assessment of four strain energy decomposition methods for phase field fracture models using quasi-static and dynamic benchmark cases

Abstract Strain energy decomposition methods in phase field fracture models separate strain energy that contributes to fracture from that which does not. However, various decomposition methods have been proposed in the literature, and it can be difficult to determine an appropriate method for a given problem. The goal of this work is to facilitate the choice of strain decomposition method by assessing the performance of three existing methods (spectral decomposition of the stress or the strain and deviatoric decomposition of the strain) and one new method (deviatoric decomposition of the stress) with several benchmark problems. In each benchmark problem, we compare the performance of the four methods using both qualitative and quantitative metrics. In the first benchmark, we compare the predicted mechanical behavior of cracked material. We then use four quasi-static benchmark cases: a single edge notched tension test, a single edge notched shear test, a three-point bending test, and a L-shaped panel test. Finally, we use two dynamic benchmark cases: a dynamic tensile fracture test and a dynamic shear fracture test. All four methods perform well in tension, the two spectral methods perform better in compression and with mixed mode (though the stress spectral method performs the best), and all the methods show minor issues in at least one of the shear cases. In general, whether the strain or the stress is decomposed does not have a significant impact on the predicted behavior.

Zhang, Shuaifang↗
DOE OSTI2025

Glauber’s Salt Composites for HVAC Applications: A Study on the Use of the T-History Method with a Modified Data Evaluation Methodology

Phase change materials (PCMs) can be utilized in buildings for peak load shifting in air conditioning systems, and the use of salt hydrate-based PCMs can reduce the cost of thermal energy storage devices. Glauber’s salt is an economical salt hydrate PCM with a melting point of around 32 °C. However, the desired melting range typically falls between 18 and 22 °C for building air conditioning applications. Although many researchers have characterized Glauber’s salt and its composites with modified melting points, enthalpy–temperature curves for composites of Glauber’s salt and NaCl are unavailable. In this study, we report the melting and solidification enthalpy–temperature curves for two different composites of Glauber’s salt and NaCl with a melting point of 21 °C obtained by the T-history method. Both composites contain NaCl to suppress the melting point, borax to reduce supercooling, and sodium polyacrylate as a thickener to enhance cyclic stability. The first composite with 12 wt.% NaCl demonstrated 139 kJ·kg−1 of latent heat of fusion, and the second composite with 9 wt.% NaCl demonstrated 171 kJ·kg−1. Both the composites have high volumetric energy densities compared to their organic counterparts with similar melting points.

Chemistry↗
DOE OSTI2021

Object-space optimization of tomographic reconstructions for additive manufacturing

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

11-th order of accuracy for numerical solution of 3-D Poisson equation with irregular interfaces on unfitted Cartesian meshes

For the first time the optimal local truncation error method (OLTEM) with 125-point stencils and unfitted Cartesian meshes has been developed in the general 3-D case for the Poisson equation for heterogeneous materials with smooth irregular interfaces. The 125-point stencils equations that are similar to those for quadratic finite elements are used for OLTEM. The interface conditions for OLTEM are imposed as constraints at a small number of interface points and do not require the introduction of additional unknowns, i.e., the sparse structure of global discrete equations of OLTEM is the same for homogeneous and heterogeneous materials. The stencils coefficients of OLTEM are calculated by the minimization of the local truncation error of the stencil equations. These derivations include the use of the Poisson equation for the relationship between the different spatial derivatives. Such a procedure provides the maximum possible accuracy of the discrete equations of OLTEM. In contrast to known numerical techniques with quadratic elements and third order of accuracy on conforming and unfitted meshes, OLTEM with the 125-point stencils provides 11-th order of accuracy, i.e., an extremely large increase in accuracy by 8 orders for similar stencils. The numerical results show that OLTEM yields much more accurate results than high-order finite elements with much wider stencils. The increased numerical accuracy of OLTEM leads to an extremely large increase in computational efficiency. Additionally, a new post-processing procedure with the 125-point stencil has been developed for the calculation of the spatial derivatives of the primary function. The post-processing procedure includes the minimization of the local truncation error and the use of the Poisson equation. It is demonstrated that the use of the partial differential equation (PDE) for the 125-point stencils improves the accuracy of the spatial derivatives by 6 orders compared to post-processing without the use of PDE as in existing numerical techniques. At an accuracy of 0.1% for the spatial derivatives, OLTEM reduces the number of degrees of freedom by 900 - 4∙10 6 times compared to quadratic finite elements. The developed post-processing procedure can be easily extended to unstructured meshes and can be independently used with existing post-processing techniques (e.g., with finite elements).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Operando Freezing Cryogenic Electron Microscopy of Active Battery Materials

Abstract Understanding structural and chemical evolution of battery materials during operation is critical to achieving safe, efficient, and long-lasting energy storage. Cryogenic electron microscopy (cryo-EM) has become a valuable tool in battery characterization, leveraging low temperatures to improve stability of sensitive materials under electron beam irradiation. However, typical cryo-EM sample preparations leave extended time between the electrochemical point of interest and ex situ freezing of samples, during which active structures may relax, degrade, or otherwise evolve. Here, we detail a method for operando freezing cryo-EM to preserve and characterize native electrode and interfacial structures that arise during battery cycling, based on an operando plunge freezer and cold sample removal process. We validate the method on multiple electrode materials and quantify and discuss the freezing rate achieved. Operando freezing cryo-EM can be used to directly visualize transient features that arise at active electrochemical interfaces, to enable deeper understanding of structural evolution and interfacial chemistry in batteries and other electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2022

Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction

Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memory footprint and a shorter reconstruction time. In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles. In addition, we propose a parallel image gradient decomposition method that enables asynchronous point-to-point communications and parallel pipelining with minimal overhead on a large number of GPUs. Our experiments on a Titanate material dataset (PbTiO3) with 16632 probe locations show that our Gradient Decomposition algorithm reduces memory footprint by 51 times. In addition, it achieves time-to-solution within 2.2 minutes by scaling to 4158 GPUs with a super-linear strong scaling efficiency at 364% compared to runtimes at 6 GPUs. This performance is 2.7 times more memory efficient, 9 times more scalable and 86 times faster than the state-of-the-art algorithm.

Wang, Xiao↗
DOE OSTI2022

Visualization of defect induced in-gap states in monolayer MoS 2

Atomic-scale intrinsic defects play a key role in controlling functional electronic properties of two-dimensional (2D) materials. Here, we present a low-temperature scanning–tunneling microscopy and spectroscopy investigation of a common point-defect in monolayer molybdenum disulfide (MoS 2 ). We employ a sample preparation method in which the film surface is never exposed to air so that the native dangling bonds surrounding the defects in the film are preserved. Molybdenum vacancies are identified by their three characteristic in-gap resonances by combining scanning–tunneling measurements with parallel Green’s function-based theoretical modeling. The relative energy shifts between the various in-gap states allow us to identify a relative charge difference between two of the observed vacancies. The role of the substrate on the band structure of the defective MoS 2 monolayer is unveiled. Our study highlights the effects of the substrate on the in-gap states of common defects found in MoS 2 providing a pathway in designing and optimizing 2D materials for electronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Review on Perovskite Solar Cells: From Single‐Junction Devices to Tandem Deployment in Space

Perovskite solar cells (PSCs) have emerged as a transformative photovoltaic technology, offering high power conversion efficiency (PCE) and the potential for cost-effective manufacturing. However, stability and large-scale manufacturing remain critical challenges that must be addressed for widespread adoption. This review provides a roadmap from single-junction perovskite solar cells to tandem deployment in space. First, material-level innovations are discussed, including mixed-cation and low-dimensional perovskites, transport materials, and additives that improve thermal and structural stability while enhancing efficiency. Then, we examine both established industrial standards and emerging scientific protocols aimed at stabilizing PSCs under operational conditions, including tandem cell integration strategies and encapsulation techniques to mitigate performance degradation. Manufacturing scalability is a focal point, where deposition methods and green solvents are explored to improve large-area film uniformity and reduce environmental impact. Additionally, the increasing viability of PSCs in extraterrestrial environments is assessed, with emphasis on their performance in space applications, radiation resistance, and flexible lamination methods for deployment in extreme conditions. Progress across materials innovation, device architectures, stability testing protocols, and both terrestrial and extraterrestrial applications collectively drives perovskite photovoltaics toward higher efficiency, stability, and cost-effectiveness.

flexible PSCs↗
DOE OSTI2022

CO 2 Laser Sintering of Garnet-Type Solid-State Electrolytes

We report the processing of garnet-type solid-state electrolytes remains challenging as densification conventionally requires high sintering temperatures and long processing times, which can result in severe Li loss, the formation of secondary phases, and thus high porosity and low ionic conductivity. Here, we report an ultrafast sintering method based on CO 2 laser scanning with the assistance of a heating stage. We demonstrate the rapid densification of low-packing-density Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) films, which are difficult to densify by conventional furnace sintering methods. This unique approach has three fingerprint characteristics: (1) mitigation of Li loss through ultrafast sintering (dwelling time $\ll$1 s); (2) a unique anisotropic shrinkage behavior that greatly reduces film thickness; (3) wave-like surface topology from point scanning strategy that enables 3D interfacial contacts with electrode materials. Herein, highly dense (95.68%) and highly conductive (0.26 mS·cm –1 at 25 °C) LLZTO films are obtained through CO2 laser sintering. This work provides a unique, scalable, and widely applicable ultrarapid laser sintering technique to overcome the difficulties associated with classic methods for the integration of SSEs for practical all-solid-state Li-metal battery applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Smart Refractory Sensors Development for Corrosion and Erosion Monitoring in High Temperature Systems

To optimize the operation and functioning of high temperature systems such as slagging gasifiers, coal boilers and glass/steel melters, it is important to monitor corrosion and erosion of refractory used in such systems. Corrosion test strategies are generally based on continuous gravimetric and chemical reactivity monitoring at operational temperatures (750°-1500°C). Both thermocouples and failure sensors and arrays would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance demonstration of smart refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The main feature of this technology is that electroceramic based sensors are embedded into smart refractory without significantly impact to the intrinsic properties of the refractory. This technology circumvents the need to insert an isolated monolithic, stand-alone sensor into the refractory via an access port. This technological approach guarantees the integrity and the chemical stability of the materials used in the sensor fabrication within the harsh environment and does not introduce molten material (such as slag) penetration pathways within the refractory. One interesting and important aspect of this innovation is that these embedded sensors can be used to in situ monitoring processes such as chemical reactions and at the same time give information and a deeper understanding of the corrosion and erosion process of the refractory within the system. As stated above, the objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). Prior to the development of the high-temperature sensors, the oxides composites developed in this study were sintered up to 1600°C under oxidizing atmosphere in order to investigate densification, microstructural evolution, phase development, and their thermoelectrical performance as a function of the composition. The 4-point DC conductivity measurements were performed between 100°-1500°C. The sensors were fabricated from the composite materials by 3D-printing or screen-printing methods into the refractory brick during the consolidation process. An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV around 1400 °C. Corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory brick an in-house glass composition was prepared and pressed into pellets and delivered into a pre-cut cavity in the brick. Corrosion experiments results showed the glass penetrated the brick over a 90 h period, and the penetration of the glass through the brick could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Control of polarization in bulk ferroelectrics by mechanical dislocation imprint

Defects are essential to engineering the properties of functional materials ranging from semiconductors and superconductors to ferroics. Whereas point defects have been widely exploited, dislocations are commonly viewed as problematic for functional materials and not as a microstructural tool. In this work, we developed a method for mechanically imprinting dislocation networks that favorably skew the domain structure in bulk ferroelectrics and thereby tame the large switching polarization and make it available for functional harvesting. The resulting microstructure yields a strong mechanical restoring force to revert electric field–induced domain wall displacement on the macroscopic level and high pinning force on the local level. This induces a giant increase of the dielectric and electromechanical response at intermediate electric fields in barium titanate [electric field–dependent permittivity (ε 33 ) ≈ 5800 and large-signal piezoelectric coefficient ( d 33 *) ≈ 1890 picometers/volt]. Dislocation-based anisotropy delivers a different suite of tools with which to tailor functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Shear Strength and Release of Large Cryogenic Pellets from the Barrel of a Shattered Pellet Injector for Disruption Mitigation

Shattered pellet injection (SPI) has been chosen as the baseline disruption mitigation system on ITER due to its ability to rapidly inject material deep into the plasma to greatly increase the plasma density and radiate the thermal energy. SPI utilizes a mechanical punch or high-pressure gas to release and accelerate a pellet that has been cryogenically desublimated in the barrel of a pipe gun. Various material injection combinations could possibly be implemented during different phases of a disruption event to radiate plasma energy, reduce electromagnetic loads on machine components, avoid the formation of runaway electrons, or to dissipate runaway electrons that form. Each injection phase could possibly utilize combinations of deuterium, neon, or argon. In this paper we outline experimental measurements of pellet material shear strength at SPI operating temperatures to understand the force needed to release SPI pellets. Deuterium, neon, argon, and deuterium-neon mixture pellets with diameters of 8.5, 12.5, and 15.7 mm are formed at a range of relevant gas pressures and temperatures and dislodged from the cold zone with a slow-moving piston driven by a motor. The slow-moving piston is kept above the triple point temperature of the material while the pellet is forming, then cooled to below the triple point temperature before contacting the pellet to minimize any thermal conduction to the pellet. The piston incorporates a load cell to measure the force applied when the pellet breaks away from the cold zone in the barrel.The ability of the gas and punch methods to exceed the shear strength of the studied pellet materials for release has been analyzed. High pressure gas delivered by fast opening valves produce pressure shock to the pellet due to supersonic expansion of the propellant gas. Pressure (and therefore, force) oscillations are present due to transverse density propagation throughout the breech volume. Mechanical punches deliver an impact force through a high-kinetic energy impact. The effect of the mechanical shock on the pellet has been explored and will be presented in this paper. Scaling to larger ITER size SPI pellets will be described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗