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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multiscale Modeling Framework for Lithium Nucleation in 3D Porous Carbon Anodes

Porous carbon scaffolds offer a promising route for mitigating non-uniform lithium (Li) plating to enhance the safety and longevity of Li metal batteries. However, the influence of microstructural morphology on Li nucleation is not well understood. Here, we present a multiscale modeling framework to investigate how the porous microstructure of carbon materials affects Li nucleation behavior. Ab initio molecular dynamics simulations quantify the nucleation energy barriers of Li on graphene as a function of Li content, surface curvature, and applied potential, providing key parameters for a classical nucleation theory (CNT) model. From macroscale half-cell simulations, we obtained Li concentration and electrical potential profiles to define boundary conditions for mesoscopic simulations. At the mesoscale, three distinct synthetic 3D microstructures with different porosities and characteristic feature sizes are generated to resolve local distributions of Li flux, current density, and mechanical stress. These outputs are integrated into the CNT model to map spatial variation in nucleation rates. Our findings reveal trade-offs between suppressing nucleation rates and achieving spatial uniformity, offering design guidelines for optimizing porous carbon anodes to balance nucleation control and mechanical integrity.

Materials science↗
DOE OSTI2024

Hydropower Flexibility Framework (Final Technical Report)

The Hydropower Flexibility Framework (HFF) tool focuses on providing the hydropower community with an effective means of assessing optimized hydropower plant outcomes. This tool combines both site specific characteristics, which act to constrain plant operation, and the hydrologic and grid characteristics which drive hydropower plant operation. The hydropower community faces a confluence of factors which drive the importance of developing such a capability, including an aging hydropower fleet subject to a range of modernization opportunities, a large number of hydropower plant relicensing activities which may affect operational requirements, an electrical grid with increasing levels of variable resources which must be balanced to maintain grid stability, and climate change influencing riverine hydrologic patterns outside of design characteristics. With support from the hydropower community, the project team developed the HFF tool and demonstrated the tool through a series of Use Cases. This guidance was developed as a part of the larger HFF tool User’s Manual (see Appendix B), a resource designed to inform other users and to empower community uptake of the tool. The HFF tool, hosted at https://hfftool.com/, was developed with the support of the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). EPRI is currently exploring alternatives to support the continued maintenance and functionally of the online tool.

13 HYDRO ENERGY↗
DOE OSTI2022

Plutonium Hybrid Materials: A Platform to Explore Assembly and Metal–Ligand Bonding

In this work, we report the synthesis of five new hybrid materials containing the [PuCl 6 ] 2- anion and charge balancing, non-covalent interaction donating 4-X-pyridinium (X = H, Cl, Br, I) cations. Single crystals of the title compounds were grown and harvested from acidic, chloride-rich, aqueous media and their structures were determined via X-ray diffraction. Compounds 1-4, (4XPyH) 2 [PuCl 6 ] and 5, (4IPyH) 4 [PuCl 6 ] · 2Cl, exhibit two distinct sheet-like structure types. Structurally relevant non-covalent interactions were tabulated from crystallographic data and verified computationally using electrostatic surface potential maps and the quantum theory of atoms in molecules (QTAIM) approach. The strength of the hydrogen and halogen bonds was quantified using Kohn-Sham density functional theory and a hierarchy of acceptor-donor pairings established. In turn, the PuIV-Cl bonds were studied using the QTAIM and natural localized molecular orbital (NLMO) approaches to delineate the underlying bond mechanism and hybrid atomic orbital contributions therein. Energy decomposition (ED) and natural ED analyses were also explored to probe the bond mechanism and, more broadly, explore the efficacy of these techniques in studying these anionic systems. The results of the PuIV-Cl bond analyses were compared across composition via analogous treatments of previously reported [PuO 2 Cl 4 ] 2- and [PuCl 3 (H 2 O) 5 ] molecular units. In summary, our study indicates that the Pu-Cl bonds are predominately ionic, yet exhibit small varying degrees of covalent character that increase from [PuCl 3 (H 2 O) 5 ], [PuO 2 Cl 4 ] 2- , to [PuCl 6 ] 2- , while the participation of the Pu based s/d and f orbitals concurrently decrease and increase, respectively.

transuranic↗
DOE OSTI2025

Computational Design of Cost-Effective High-Entropy Thermal/Environmental Barrier Coatings

Developing cost-effective thermal/environmental barrier coatings (TEBC) requires balance among various properties including low thermal conductivity, matching coefficient of thermal expansion (CTE), high thermal stability, high fracture toughness, and high recession resistance while being affordable. Low oxygen diffusivity is desirable as it can slow down oxygen transport to reach the underlying bond coating and hence delay oxidation of the bond coating. This project aims to design low-cost high-performance TEBC based on high entropy rare earth disilicates to protect SiC-based ceramic matrix composites from chemical and thermal attack for better performance of components in the hot section of gas turbine engines. To accelerate the TEBC design, we utilize first-principles density functional theory to predict key properties including phase stability, CTE, lattice thermal conductivity, temperature-dependent elastic constants, and oxygen diffusivity. Alloying elements including Yb, Y, Er, Eu, Gd, Lu, La, and Ce are considered, and modeling prediction are compared with available experimental results.

coefficient of thermal expansion↗
DOE OSTI2026

Influence of Nanoconfinement and Elevated Temperatures on Geocolloid-Facilitated Transport of Energy-related Contaminants

The main objective of this project is to obtain a better understanding of the transport behavior and interactions of geocolloids in the presence of energy- related contaminants under bulk and nanoconfined conditions. In that, we first established solid protocols to synthesize and fabricate numerous types of geocolloids with a. various sizes ranging from 40 – 800 nm and b. different hydrophile-lipophile balance (HLB) ratios in the range of 25:75 to 75:25. Direct force measurements with geocolloids having different degrees of surface coverages (i.e. HLB to realistically mimic adsorption of energy related contaminants) were conducted using the Surface Forces Apparatus (SFA) over a distance regime starting from 8μm all the way down to molecular contact. Repulsive forces were observed on approach starting from > 3μm, followed by an exponential increase of which magnitude appears to be larger than a decay length obtained from Derjaguin–Landau–Verwey–Overbeek (DLVO) theory in pure water. When the geocolloids were confined in salted water, the magnitude of onset of repulsion was varied as a function of salinity in solution, which can significantly alter a purely repulsive screened electrostatic (coulombic) interaction arising from, among geocolloids as well as between geocolloids and geosurfaces. The viscosity and flow characteristics of geocolloidal suspensions at different degrees of confinement were also investigated where we identified highly discontinuous rheological behaviors below a critical nanoconfinement level. We anticipate that the knowledge gained through this study will enable the scientists and researchers to better assess transport and fate behaviors of geocolloidal dispersions that can carry energy-related contaminants under realistically emulated geosystem.

03 NATURAL GAS↗
DOE OSTI2024

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and structure diversity, which results in a wide range of specificities for several different ion channels and receptors. Despite the recognized importance of these compounds, our ability to determine their binding targets and toxicities remains a significant challenge. Predicting the target receptors of conotoxins, based solely on their amino acid sequence, remains a challenge due to the intricate relationships between structure, function, target specificity, and the significant conformational heterogeneity observed in conotoxins with the same primary sequence. We have previously demonstrated that the inclusion of post-translational modifications, collisional cross sections values, and other structural features, when added to the standard primary sequence features, improves the prediction accuracy of conotoxins against non-toxic and other toxic peptides across varied datasets and several different commonly used machine learning classifiers. Here, we present the effects of these features on conotoxin class and molecular target predictions, in particular, predicting conotoxins that bind to nicotinic acetylcholine receptors (nAChRs). We also demonstrate the use of the Synthetic Minority Oversampling Technique (SMOTE)-Tomek in balancing the datasets while simultaneously making the different classes more distinct by reducing the number of ambiguous samples which nearly overlap between the classes. In predicting the alpha, mu, and omega conotoxin classes, the SMOTE-Tomek PCA PLR model, using the combination of the SS and P feature sets establishes the best performance with an overall accuracy (OA) of 95.95%, with an average accuracy (AA) of 93.04%, and an f1 score of 0.959. Using this model, we obtained sensitivities of 98.98%, 89.66%, and 90.48% when predicting alpha, mu, and omega conotoxin classes, respectively. Similarly, in predicting conotoxins that bind to nAChRs, the SMOTE-Tomek PCA SVM model, which used the collisional cross sections (CCSs) and the P feature sets, demonstrated the highest performance with 91.3% OA, 91.32% AA, and an f1 score of 0.9131. The sensitivity when predicting conotoxins that bind to nAChRs is 91.46% with a 91.18% sensitivity when predicting conotoxins that do not bind to nAChRs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Beyond pinball loss: Quantile methods for calibrated uncertainty quantification

Amongthemanywaysofquantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A model that predicts the true conditional quantiles for each input, at all quantile levels, presents a correct and efficient representation of the underlying uncertainty. To achieve this, many current quantile-based methods focus on optimizing the pinball loss. However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e.g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles. In this work, we develop new quantile methods that address these shortcomings. In particular, we propose methods that can apply to any class of regression model, select an explicit balance between calibration and sharpness, optimize for calibration of centered intervals, and produce more accurate conditional quantiles. We provide a thorough experimental evaluation of our methods, which includes a high dimensional uncertainty quantification task in nuclear fusion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Role of an ancient light-harvesting protein of PSI in light absorption and photoprotection

Diverse algae of the red lineage possess chlorophyll a-binding proteins termed LHCR, comprising the PSI light-harvesting system, which represent an ancient antenna form that evolved in red algae and was acquired through secondary endosymbiosis. However, the function and regulation of LHCR complexes remain obscure. Here we describe isolation of a Nannochloropsis oceanica LHCR mutant, named hlr1, which exhibits a greater tolerance to high-light (HL) stress compared to the wild type. We show that increased tolerance to HL of the mutant can be attributed to alterations in PSI, making it less prone to ROS production, thereby limiting oxidative damage and favoring growth in HL. HLR1 deficiency attenuates PSI light-harvesting capacity and growth of the mutant under light-limiting conditions. We conclude that HLR1, a member of a conserved and broadly distributed clade of LHCR proteins, plays a pivotal role in a dynamic balancing act between photoprotection and efficient light harvesting for photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

How Thermodynamic, Electronic, and Steric Factors Influence Mesitylcopper Oligomers

Mesitylcopper (CuMes) is a highly versatile organocopper reagent used in both organic and inorganic syntheses. It has previously been shown that CuMes exists as a tetrameric or pentameric cyclic oligomer [CuMes] n (n = 4, 5), both in solution and in the solid state. The bonding arrangement between the [CuMes] units has qualitatively been described as localized three-center twoelectron (3c-2e) bonds. However, the electronic, structural, and thermodynamic forces driving this aggregation are still not well understood. For this reason, we employed density functional theory (DFT) calculations to study mesitylcopper as a monomeric [CuMes] unit and [CuMes]n oligomers with n = 2 to n = 7. We found that there is a strong electronic driving force for aggregation caused by strong mixing between the Cu’s d orbitals and Mes’s π orbitals in oligomers larger than the dimer. This mixing is only optimized in oligomers with n ≥ 3, where the mesityl group is no longer bonded to a single copper center but instead becomes a bridging ligand. Beyond the trimer, steric and entropic factors become relevant for determining the relative stabilities of the different aggregates, with midsized oligomers (n = 4−5) having the optimal balance between the electronic Cu−C bonding character, Cu···Cu attractive forces, entropy, reduced internal ring strain, and reduced steric interactions between the mesityl groups.

Copper↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Windthrow characteristics and their regional association with rainfall, soil, and surface elevation in the Amazon

Abstract Windthrows (trees uprooted and broken by winds) are common across the Amazon. They range in size from single trees to large gaps that lead to changes in forest dynamics, composition, structure, and carbon balance. Yet, the current understanding of the spatial variability of windthrows is limited. By integrating remote sensing data and geospatial analysis, we present the first study to examine the occurrence, area, and direction of windthrows and the control that environmental variables exert on them across the whole Amazon. Windthrows are more frequent and larger in the northwestern Amazon (Peru and Colombia), with the central Amazon (Brazil) being another hot spot of windthrows. The predominant direction of windthrows is westward. Rainfall, surface elevation, and soil characteristics explain the variability (20%–50%) of windthrows but their effects vary regionally. A better understanding of the spatial dynamics of windthrows will improve understanding of the functioning of Amazon forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimization of the moderators in the STS preliminary design

This report details the results for an optimization of the dimensions of the moderators in the preliminary design of the Spallation Neutron Source Second Target Station (STS). This study uses the optimization algorithms of Dakota and an unstructured mesh model for the moderators in MCNP. More details on the unstructured mesh model and the automated mesh generation can be found in [3]. Parallel to this effort, the same moderator geometries have been optimized using a constructive solid geometry (CSG) MCNP model. More details on this model and its results can be found in [4]. Three optimal designs are selected for each moderator: one that is optimized for maximum peak brightness, one for maximum time-integrated brightness, and one for a combination of peak and time-integrated brightness. The backbone of the optimization work flow is provided by Dakota. For each set of design parameters requested by Dakota, a new solid geometry is automatically built in Creo and SpaceClaim, and subsequently exported to Attila4MC to generate an unstructured mesh geometry for MCNP. After the MCNP calculation is finished, the objective function (e.g., brightness metric) is returned to Dakota. After the new design has been evaluated, a result-file is written, and Dakota proposes the next set of design parameters to be evaluated. The loop continues until a specified convergence criterion has been met. The design parameters of the cylindrical (upper) moderator include the hydrogen radius, the premoderator thickness (top, bottom, radial), the beryllium radius and the horizontal position of the moderator. The crucial design choice is the hydrogen radius. A radius of 62 mm is shown to provide the maximum time-integrated brightness. The maximum peak brightness occurs with a radius of 40 mm. A combined (middle) design, which balances peak and time-integrated brightnesses, is obtained with a hydrogen radius of 50 mm. The premoderator thicknesses and the beryllium radius are slightly larger in the design optimized for time-integrated brightness than in the design optimized for peak brightness. The sensitivity to these two parameters is relatively small close to the optimal configurations. The hydrogen vessel and vacuum vessel wall thicknesses are dependent on the radius of the liquid hydrogen due to structural integrity requirements. The increased wall thicknesses for larger vessels significantly penalize the time-integrated brightness, with the maximum obtainable value reduced by more than 10% relative to earlier studies which used fixed vessel wall thicknesses. The impact of the variable wall thicknesses is much less for the peak brightness and combined brightness designs. The design parameters of the tube (lower) moderator selected for the optimization are the tube length, the annular premoderator thickness, the beryllium radius and the horizontal position of the moderator. The tube length is the crucial parameter and is chosen large (210 mm) and small (125 mm) in the designs optimized for time-integrated and peak brightness respectively. A combined optimal design has a tube length of 170 mm. The premoderator thickness and the beryllium radius are chosen larger in the design optimized for time-integrated brightness.

42 ENGINEERING↗
DOE OSTI2022

All‐Polymer Bulk‐Heterojunction Organic Electrochemical Transistors with Balanced Ionic and Electronic Transport

Abstract The rapid development of organic electrochemical transistor (OECTs)‐based circuits brings new opportunities for next‐generation integrated bioelectronics. The all‐polymer bulk‐heterojunction (BHJ) offers an attractive, inexpensive alternative to achieve efficient ambipolar OECTs, and building blocks of logic circuits constructed from them, but have not been investigated to date. Here, the first all‐polymer BHJ‐based OECTs are reported, consisting of a blend of new p‐type ladder conjugated polymer and a state‐of‐the‐art n‐type ladder polymer. The whole ladder‐type polymer BHJ also proves that side chains are not necessary for good ion transport. Instead, the polymer nanostructures play a critical role in the ion penetration and transportation and thus in the device performance. It also provides a facile strategy and simplifies the fabrication process, forgoing the need to pattern multiple active layers. In addition, the development of complementary metal–oxide–semiconductor (CMOS)‐like OECTs allows the pursuit of advanced functional logic circuitry, including inverters and NAND gates, as well as for amplifying electrophysiology signals. This work opens a new approach to the design of new materials for OECTs and will contribute to the development of organic heterojunctions for ambipolar OECTs toward high‐performing logic circuits.

Chemistry↗
DOE OSTI2021

MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids

In this report, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Highly Emissive Semi-Ladder-Type Copolymers, Aggregation State, and Solution-Processed Organic Light-Emitting Transistor

An organic light-emitting transistor (OLET) integrates the logic function of a field-effect transistor (FET) with electroluminescence of a light-emitting diode (LED) into a single device. This has the potential to play an important role in smart displays, electrically pumped organic laser, and other flexible electronic devices. For achieving high-performance solution-processed OLET, three semiladder copolymers—TPTI-CC, TPTI-C, and TPTI-F—were developed in this study. It was found that variation in the chemical structure lead to significant differences in energy level, emission color, and intermolecular aggregation. High photoluminescence quantum yields (PLQY) was obtained in these three polymers. Solution-processed trilayered OLET devices were fabricated, which exhibited strong electroluminescence, balanced charge mobility, and external quantum efficiency (EQE) of 2.8%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the Reactivity, Selectivity, and Deactivation of Frustrated Lewis Pairs for Semihydrogenation of Acetylene

A good catalyst for semihydrogenation of alkynes must preclude both over-hydrogenation of alkene to alkane and isomerization to the other alkene isomer. In addition, it should balance the trade-off between selectivity and activity. In 2013, the Repo and Pápai groups reported a frustrated Lewis pair (FLP) (1-NMe 2 -2-B(C 6 F 5 ) 2 -C 6 H 4 ), 1, which is a metal-free catalyst and for the first time shows excellent reactivity for the hydrogenation of internal alkynes. However, it is unreactive for terminal alkynes. In this work, we have designed 13 FLPs, a–m, based on 1 by varying the Lewis base site with N and P and the Lewis acid site with B, Al, Ga, and In and replacing pentafluorophenyl with 1,3,5-trifluorophenyl, phenyl, or trifluoromethyl. We apply density functional theory to study the activity, selectivity, and deactivation of FLP 1-m for acetylene semihydrogenation. The catalytic cycle consists of three steps: (1) alkyne insertion, (2) H 2 heterolysis, and (3) intramolecular protonation. We found the activity does not change much by the modification of bulky ligands, while it decreases with the direct replacement of LA and LB sites. The overall activity depends on steps 1 and 3, which are, respectively, positively and negatively linear correlated with the charge of the Lewis acid site. Most of the FLPs in this work show comparable or better selectivity for semihydrogenation of acetylene than 1. FLP deactivation is due to the strong binding of acetylene and the elimination of electron-withdrawing bulky ligands at the preactivated catalyst rather than at activated catalysts. Furthermore, taking the selectivity and stability of FLPs into account, we predict d and k are potentially active for terminal alkynes.

02 PETROLEUM↗
DOE OSTI2023

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
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↗