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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Using Ambient Noise Seismic Interferometry and Local and Teleseismic Earthquakes to Determine Crustal Thickness and Moho Structure of the Northwestern Gulf of Mexico Margin

Abstract The northwestern part of the Gulf of Mexico has undergone two episodes of continental rifting and collision and produced structural artifacts that are now buried under many kilometers of sediments, complicating investigations of the region. The deep sedimentary package precludes outcrops and points to a need for the application of seismic techniques, but low rates of seismicity in the region and sparse seismic monitoring limit the utility of traditional seismic methods. We therefore use diverse data to perform two‐dimensional seismic tomography across the dry land portion of the margin. Data are gleaned from teleseismic and regional earthquakes, postcritical SsPmp arrivals, and direct P wave energy identified with seismic interferometry that were recorded by a broadband, three‐component array and partially overlapping short‐period, vertical‐component array. The Pn and postcritical SsPmp phase help constrain the Moho discontinuity, which a previous receiver function study suggested was absent beneath the seaward portion of this transect. A high‐velocity body is observed in the crust at the same location as the Houston Magnetic Anomaly, possibly marking rocks from the Alleghenian continental assembly. The crust thins from NW to SE, indicating that extension occurred mostly to the south of the Ouachita orogeny. Our model indicates that the margin's sediment package reaches a maximum thickness of ~15 km at the coast and becomes unresolvably thin near the Llano Uplift.

58 GEOSCIENCES↗
DOE OSTI2024

Weak-form inference for hybrid dynamical systems in ecology

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Direct numerical simulation of reactor two-phase flows enabled by high-performance computing

Nuclear reactor two-phase flows remain a great engineering challenge, where the high-resolution two-phase flow database which can inform practical model development is still sparse due to the extreme reactor operation conditions and measurement difficulties. Owing to the rapid growth of computing power, the direct numerical simulation (DNS) is enjoying a renewed interest in investigating the related flow problems. A combination between DNS and an interface tracking method can provide a unique opportunity to study two-phase flows based on first principles calculations. More importantly, state-of-the-art high-performance computing (HPC) facilities are helping unlock this great potential. This paper reviews the recent research progress of two-phase flow DNS related to reactor applications. The progress in large-scale bubbly flow DNS has been focused not only on the sheer size of those simulations in terms of resolved Reynolds number, but also on the associated advanced modeling and analysis techniques. Specifically, the current areas of active research include modeling of sub-cooled boiling, bubble coalescence, as well as the advanced post-processing toolkit for bubbly flow simulations in reactor geometries. A novel bubble tracking method has been developed to track the evolution of bubbles in two-phase bubbly flow. Also, spectral analysis of DNS database in different geometries has been performed to investigate the modulation of the energy spectrum slope due to bubble-induced turbulence. In addition, the single-and two-phase analysis results are presented for turbulent flows within the pressurized water reactor (PWR) core geometries. The related simulations are possible to carry out only with the world leading HPC platforms. Furthermore, these simulations are allowing more complex turbulence model development and validation for use in 3D multiphase computational fluid dynamics (M-CFD) codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Synthetic method of analogues for emerging infectious disease forecasting

The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Subtask 1.4 - Techno-Economic Assessment of Regional Carbon Utilization Scenarios and Attendant Monitoring Technology

The U.S. Department of Energy (DOE) Carbon Storage Program seeks to develop technologies to significantly improve the effectiveness of carbon capture and storage (CCS), reduce the cost of implementation, and support the readiness of widespread commercial deployment by 2035. To accomplish this goal, technical and economic barriers must be addressed and the results used to inform regulators and industry on the safety, permanence, and viability of carbon storage. To help overcome these barriers, the Energy & Environmental Research Center (EERC) conducted three activities: 1) investigate the regional techno-economic life cycle of CO2 enhanced oil recovery (EOR) in North Dakota, 2) assist DOE in determining the prospective CO2 storage resource of residual oil zones (ROZs), and 3) develop a seismic technique for monitoring geologic storage of CO2. These three activities address two strategic goals enumerated in the DOE–EERC Cooperative Agreement, namely, 1) to advance foundational science and 2) to inform data-driven policies that enhance U.S. economic growth and job creation, energy security, and environmental quality. Activity 1 In North Dakota, there is synergistic incentive to develop and implement CO2 EOR technologies. Regional industry partners are actively seeking options that can cost-effectively improve the efficiency of power production, while also coproducing CO2 for use in EOR applications. A full-value-chain techno-economic assessment that links lignite resources, power generation, and EOR is needed to determine the implications for a state or region to continue to use these resources or look elsewhere for other energy sources and revenue streams. The integration of North Dakota’s lignite power industry with the state’s oil industry has the potential to revitalize legacy oil fields and produce up to 1 billion barrels of additional oil using CO2 EOR while providing a market for a CO2 commodity captured at coal-fired power plants. Revitalization of North Dakota’s legacy oil fields will bring new industry investment to the state, resulting in the growth of oil-related jobs and revenue through oil sales, severance tax, and associated oilfield development activities (drilling, pipelines, etc.). CO2 demand for EOR and incremental oil production were calculated for 201 conventional oil fields in North Dakota. Infrastructure development in the fields was estimated using 1) a traditional vertical well arrangement of CO2 injectors and oil producers and 2) an advanced case where horizontal wells were used exclusively. Pipeline, compression, and recycling infrastructure requirements were also estimated. When viewed from the CO2 capture and CO2 EOR sectors, the advanced case showed a positive investment scenario when including potential benefits of the 45Q tax credit policy. An economic evaluation of a full implementation of CO2 EOR showed an average $60 billion potential state economic impact estimated over the 37-year construction and production time frame could be realized with an assumed oil price of $65/bbl. Oil price ranges of $35–$90/bbl cause an estimated variation of ±$5 billion over the potential effort. The total net state government revenues were estimated over the 37-year construction and production time frame. With an assumed oil price of $65/bbl, total potential fiscal impact could average $7.4 billion. This result ranges about ±$2.5 billion for a potential EOR implementation effort for oil price ranges of $35–$90/bbl. This broad-brush assessment was focused on “magnitude-of-effect” perspectives of what CO2 capture and use for EOR could mean to the state. Whether CO2 EOR ever becomes widely adopted is not the issue at this stage of evaluation. The question being answered is, How valuable could CO2 capture and CO2 EOR be for the state and local jurisdictions in western North Dakota? The results of the current economic assessment remove any ambiguity to that question. Full implementation of CO2 EOR as modeled in this study provides substantial economic benefits to the state, even at low oil prices (i.e., $35/bbl). Job creation and government revenues could be substantial, depending on the scale of implementation. With a better understanding of what CO2 EOR might mean for the state’s economy, the focus can now shift to the policies and financial mechanisms needed to make private investment in CO2 capture and use as EOR competitive with other energy-related pursuits. Activity 2 ROZs contain remnants of oil not swept away by natural waterflooding. Although nearly all of the identified ROZs are found in West Texas, there is evidence that this category of oil reservoir can be found in other major oil-producing basins of the United States. Because the formation of these oil reservoirs is distinct from other conventional reservoirs, the method to estimate the prospective CO2 storage resource in ROZs may be equally distinct from the method used for conventional fields. As such, the EERC collaborated with DOE’s National Energy Technology Laboratory (NETL) to investigate the feasibility of CO2 storage in ROZs and determine what specific modification (if any) needs to be made to the current DOE volumetric method to estimate prospective CO2 storage resource in oil reservoirs. This activity built on a previous EERC investigation to identify ROZs in the Williston and Powder River Basins and upon collaborations between the EERC and NETL to develop storage resource estimation methods for different types of geologic CO2 storage. Review of the volumetric approach raised questions about the applicability of such an approach to hydrocarbon-bearing zones without the inclusion of potential hydrocarbon production. A production-based approach was presented by the EERC to inform the working group of additional methods of increasing the CO2 storage resource potential. Because the production-based method focuses more heavily on oil recovery than CO2 storage, it was not included in the working group manuscript. Activity 3 The EERC has used sparse and irregular seismic arrays to detect CO2 injected into a reservoir. The variability of seismic noise recorded by the sensors directly impacts the seismic interpretation. However, designing noise attenuation workflows effectively can be a more challenging task using sparse and irregular arrays for data acquisition. Based on the hypothesis that noncoherent noise is stationary during weekly data acquisition, a new machine-learning algorithm was developed to automatically estimate noise conditions using passive records, noise from active records, and ancillary information. The new noise attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. Results show spatial and temporal noise patterns can be related to the location of the sensors and the weather conditions during the data acquisition and that our attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. This effort was funded in part through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Department of Commerce.

Peck, Wesly↗
DOE OSTI2018

Human Sensorimotor Cortex Control of Directly Measured Vocal Tract Movements during Vowel Production

During speech production, we make vocal tract movements with remarkable precision and speed. Our understanding of how the human brain achieves such proficient control is limited, in part due to the challenge of simultaneously acquiring high-resolution neural recordings and detailed vocal tract measurements. To overcome this challenge, we combined ultrasound and video monitoring of the supralaryngeal articulators (lips, jaw, and tongue) with electrocorticographic recordings from the cortical surface of 4 subjects (3 female, 1 male) to investigate how neural activity in the ventral sensory-motor cortex (vSMC) relates to measured articulator movement kinematics (position, speed, velocity, acceleration) during the production of English vowels. We found that high-gamma activity at many individual vSMC electrodes strongly encoded the kinematics of one or more articulators, but less so for vowel formants and vowel identity. Neural population decoding methods further revealed the structure of kinematic features that distinguish vowels. Encoding of articulator kinematics was sparsely distributed across time and primarily occurred during the time of vowel onset and offset. In contrast, encoding was low during the steady-state portion of the vowel, despite sustained neural activity at some electrodes. Significant representations were found for all kinematic parameters, but speed was the most robust. These findings enabled by direct vocal tract monitoring demonstrate novel insights into the representation of articulatory kinematic parameters encoded in the vSMC during speech production.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗
DOE OSTI2021

Open momentum space method for the Hofstadter butterfly and the quantized Lorentz susceptibility

Here we develop a generic k ∙ p open momentum space method for calculating the Hofstadter butterfly of both continuum (moiré) models and tight-binding models, where the quasimomentum is directly substituted by the Landau level (LL) operators. By taking a LL cutoff (and a reciprocal lattice cutoff for continuum models), one obtains the Hofstadter butterfly with in-gap spectral flows. For continuum models such as the moiré model for twisted bilayer graphene, our method gives a sparse Hamiltonian, making it much more efficient than existing methods. The spectral flows in the Hofstadter gaps can be understood as edge states on a momentum space boundary, from which one can determine the two integers (t ν , s ν ) of a gap ν satisfying the Diophantine equation. The spectral flows can also be removed to obtain a clear Hofstadter butterfly. While t ν is known as the Chern number, our theory identifies s ν as a dual Chern number for the momentum space, which corresponds to a quantized Lorentz susceptibility γ xy = eBs ν .

2-dimensional systems↗
DOE OSTI2019

Sparse sampling methods and probe systems for analytical instruments

Sparse sampling approaches and probe systems for analytical instruments are disclosed providing for effective sub-sampling of a specimen and inpainting to reconstruct representations of actual information. The sub-sampling involves serial acquisition of contiguous measured values lying at positions along a scan path extending in a line toward a first direction and having random perturbations in a second direction. The perturbations are limited within a predetermined distance from the line. Inpainting techniques are utilized among the measured values to reconstruct a representation of actual information regarding the specimen.

Kovarik, Libor↗
DOE OSTI2021

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional 3D data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popular ML compression methods, as well as conventional data compression methods, suboptimal. To address these obstacles, this work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE). This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. To achieve similar fidelity, the best performer among the traditional methods can reach only half the compression ratio of BCAE. Moreover, a thorough ablation study of the BCAE method shows that a dedicated segmentation decoder improves the reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computation-guided optimization of split protein systems

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. Furthermore, this new technology will streamline the generation and use of split protein systems for diverse applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-Driven Closures and Assimilation for Stiff Multiscale Random Dynamics

Here, we introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in closed form. Moreover, such an equation would require as many phase-space variables as there are states in the RODE system. To alleviate this curse of dimensionality, we instead derive exact, albeit unclosed, reduced-order PDF (RoPDF) equations for low-dimensional observables/quantities of interest. The unclosed terms take the form of state-dependent conditional expectations, which are directly estimated from data at sparse observation times. However, for systems exhibiting stiff, multiscale dynamics, data sparsity introduces regression discrepancies that compound during RoPDF evolution. This is overcome by introducing a kinetic-like defect term to the RoPDF equation, which is learned by assimilating in sparse, low-fidelity RoPDF estimates. Two assimilation methods are considered, namely nudging and deep neural networks, which are successfully tested against Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evaluation of multiple tracer methods to estimate low groundwater flow velocities

Here, four different tracer methods were used to estimate groundwater flow velocity at a multiple-well site in the saturated alluvium south of Yucca Mountain, Nevada: (1) two single-well tracer tests with different rest or “shut-in” periods, (2) a cross-hole tracer test with an extended flow interruption, (3) a comparison of two tracer decay curves in an injection borehole with and without pumping of a downgradient well, and (4) a natural-gradient tracer test. Such tracer methods are potentially very useful for estimating groundwater velocities when hydraulic gradients are flat (and hence uncertain) and also when water level and hydraulic conductivity data are sparse, both of which were the case at this test location. The purpose of the study was to evaluate the first three methods for their ability to provide reasonable estimates of relatively low groundwater flow velocities in such low-hydraulic-gradient environments. The natural-gradient method is generally considered to be the most robust and direct method, so it was used to provide a “ground truth” velocity estimate. However, this method usually requires several wells, so it is often not practical in systems with large depths to groundwater and correspondingly high well installation costs. The fact that a successful natural gradient test was conducted at the test location offered a unique opportunity to compare the flow velocity estimates obtained by the more easily deployed and lower risk methods with the ground-truth natural-gradient method. The groundwater flow velocity estimates from the four methods agreed very well with each other, suggesting that the first three methods all provided reasonably good estimates of groundwater flow velocity at the site. We discuss the advantages and disadvantages of the different methods, as well as some of the uncertainties associated with them.

58 GEOSCIENCES↗
DOE OSTI2019

SWinzip v. 2.0

SWinzip is a Matlab and C++ library for scientific lossy data compression and reconstruction using compressed sensing and tree-wavelets transforms. These methods are known for their large compression and usefulness in data analytics such as features extraction. Compressed sensing and wavelets methods rely heavily on sparse and dense linear algebra operations implemented through the Boost codes in our library. SWinzip accommodates data represented on both regular grids (e.g. image data) and point-clouds (e.g. unstructured meshes). In SWinzip v2.0, there is more focus on wavelet compression and the following features are added: - The matrix-free (direct) wavelet transform is implemented in both C++ and Matlab. A threshold-based compression can now be used. Examples for distributed compression are also added. - Python implementation of Alpert wavelet transforms - Field data comparison using wavelets in Matlab with global and local metrics

Solloum, Maher↗
DOE OSTI2020

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multinucleon structure and dynamics via quantum computing

We propose a framework for computing the structure and dynamics for second-quantized many-nucleon Hamiltonians on quantum computers. We develop an oracle-based Hamiltonian input model that computes the many-nucleon states and nonzero Hamiltonian matrix elements of the many-nucleon system. With our Fock-state based input model, we show how to implement the sparse matrix simulation algorithms to calculate the dynamics of the second-quantized many-nucleon Hamiltonian. Based on the dynamics simulation methods, we also present the methodology for structure calculations of the many-nucleon system. In this work, we provide an explicit circuit design of our input model of the second-quantized Hamiltonian within a direct encoding scheme that maps the occupation of each available single-particle state in the many-nucleon state to the state of specific qubit in a quantum register. Here, we analyze our method and provide the asymptotic cost in computing resources for structure and dynamics calculations of many-nucleon systems. For pedagogical purposes, we demonstrate our input model with two model problems in restricted model spaces.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗