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Results for “Dynamic feature selection”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Block orbital-selective Mott insulators: A spin excitation analysis

We present a comprehensive study of the spin excitations—as measured by the dynamical spin structure factor S(q,ω)—of the so-called block-magnetic state of low-dimensional orbital-selective Mott insulators. We realize this state via both a multi-orbital Hubbard model and a generalized Kondo-Heisenberg Hamiltonian. Due to various competing energy scales present in the models, the system develops periodic ferromagnetic islands of various shapes and sizes, which are antiferromagnetically coupled. The 2×2 particular case was already found experimentally in the ladder material BaFe2Se3 that becomes superconducting under pressure. Here we discuss the electronic density as well as Hubbard and Hund coupling dependence of S(q,ω) using density matrix renormalization group method. Several interesting features were identified: (1) An acoustic (dispersive spin-wave) mode develops. (2) The spin-wave bandwidth establishes a new energy scale that is strongly dependent on the size of the magnetic island and becomes abnormally small for large clusters. (3) Optical (dispersionless spin excitation) modes are present for all block states studied here. In addition, a variety of phenomenological spin Hamiltonians have been investigated but none matches entirely our results that were obtained primarily at intermediate Hubbard U strengths. Our comprehensive analysis provides theoretical guidance and motivation to crystal growers to search for appropriate candidate materials to realize the block states, and to neutron scattering experimentalists to confirm the exotic dynamical magnetic properties unveiled here, with a rich mixture of acoustic and optical features.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Spin Coupling Effect on Geometry-Dependent X-Ray Absorption of Diradicals

Here, we theoretically investigate the influence of diradical electron spin coupling on the time-resolved X-ray absorption spectra of the photochemical ring opening of furanone. We predict geometry-dependent carbon K-edge signals involving transitions from core orbitals to both singly and unoccupied molecular orbitals. The most obvious features of the ring opening come from the carbon atom directly involved in the bond breaking through its transition to both the newly formed singly occupied and the available lowest unoccupied molecular orbitals (SOMO and LUMO, respectively). In addition to this primary feature, the singlet spin coupling of four unpaired electrons that arises in the core-to-LUMO states creates additional geometry dependence in some spectral features with both oscillator strengths and relative excitation energies varying observably as a function of the ring opening. We attribute this behavior to a spin-occupancy-induced selection rule, which occurs when singlet spin coupling is enforced in the diradical state. Notably, one of these geometry-sensitive core-to-LUMO transitions excites core electrons from a backbone carbon not involved in the bond breaking, providing a novel nonlocal X-ray probe of chemical dynamics arising from electron spin coupling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Perturbation theory under the truncated Wigner approximation: How system-environment entanglement formation drives quantum decoherence

In discrete quantum systems, quantum decoherence is the disappearance of simple phase relations as a result of interactions with an environment. For many applications, the question is not necessarily how to avoid (inevitable) system-environment interactions, but rather how to design environments that optimally preserve a system's phase relations in spite of such interactions. The formation of system-environment entanglement is a major driving mechanism for decoherence, and a detailed understanding of this process could inform strategies for conserving coherence optimally. This requires scalable, flexible, and systematically improvable quantum dynamical methods that retain detailed information about the entanglement properties of the environment, yet very few current methods offer this combination of features. Here, in this work, we address this need by introducing a theoretical framework wherein we combine the truncated Wigner approximation with standard time-dependent perturbation theory, allowing for computing expectation values of operators in the combined system-environment Hilbert space. We demonstrate the utility of this framework by applying it to the spin-boson model, representative of qubits and simple donor-acceptor systems. For this model, our framework provides an analytical description of perturbative contributions to expectation values. We monitor how quantum decoherence at zero temperature is accompanied by entanglement formation with individual environmental degrees of freedom. Based on this entanglement behavior, we find that the selective suppression of low-frequency environmental modes is particularly effective for mitigating quantum decoherence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗
DOE OSTI2021

Liquid-induced topological transformations of cellular microstructures

The fundamental topology of cellular structures—the location, number and connectivity of nodes and compartments—can profoundly affect their acoustic, electrical5, chemical, mechanical and optical properties, as well as heat, fluid and particle transport. Approaches that harness swelling, electromagnetic actuation and mechanical instabilities in cellular materials have enabled a variety of interesting wall deformations and compartment shape alterations, but the resulting structures generally preserve the defining connectivity features of the initial topology. Achieving topological transformation presents a distinct challenge for existing strategies: it requires complex reorganization, repacking, and coordinated bending, stretching and folding, particularly around each node, where elastic resistance is highest owing to connectivity. Here we introduce a two-tiered dynamic strategy that achieves systematic reversible transformations of the fundamental topology of cellular microstructures, which can be applied to a wide range of materials and geometries. Our approach requires only exposing the structure to a selected liquid that is able to first infiltrate and plasticize the material at the molecular scale, and then, upon evaporation, form a network of localized capillary forces at the architectural scale that ‘zip’ the edges of the softened lattice into a new topological structure, which subsequently restiffens and remains kinetically trapped. Reversibility is induced by applying a mixture of liquids that act separately at the molecular and architectural scales (thus offering modular temporal control over the softening–evaporation–stiffening sequence) to restore the original topology or provide access to intermediate modes. Guided by a generalized theoretical model that connects cellular geometries, material stiffness and capillary forces, we demonstrate programmed reversible topological transformations of various lattice geometries and responsive materials that undergo fast global or localized deformations. In conclusion, we then harness dynamic topologies to develop active surfaces with information encryption, selective particle trapping and bubble release, as well as tunable mechanical, chemical and acoustic properties.

42 ENGINEERING↗
DOE OSTI2024

Coherent spin wave excitation with radio-frequency spin–orbit torque

Spin waves, collective perturbations of magnetic moments, are both fundamental probes for magnetic physics and promising candidates for energy-efficient signal processing and computation. Traditionally, coherent propagating spin waves have been generated by radio frequency (RF) inductive Oersted fields from current-carrying electrodes. An alternative mechanism, spin–orbit torque (SOT), offers more localized excitation through interfacial spin accumulation but has been mostly limited to DC to kHz frequencies. SOT driven by RF currents, with potentially enhanced pumping efficiency and unique spin dynamics, remains largely unexplored, especially in magnetic insulators. Here, we conduct a comprehensive theoretical and computational investigation into the generation of coherent spin waves via RF-SOT in the prototypical yttrium iron garnet. We characterize the excitation of forward volume, backward volume, and surface modes in both linear and nonlinear regimes, employing single and interdigitated electrode configurations. We reveal and explain several unique and surprising features of RF-SOT compared to inductive excitation, including higher efficiency, distinct mode selectivity, and directional symmetry, a ~ $3π/4$ phase offset, reduced anharmonic distortion in the nonlinear regime, and the absence of second harmonic generation. These insights position RF-SOT as a promising new mechanism for future magnonic and spintronic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Plant Synthetic Biology Conference – Travel Awardees and Speakers

The overarching goals of the Plant Synthetic Biology (SynBio) Conference, convened by the American Society of Plant Biologists (ASPB), were to raise community consciousness of what SynBio is and what it can do in key areas, including crop productivity, specialty chemicals, biosensors, and microbe-plant interactions. Within that context, the conference had two broad objectives. First, to feature a set of leading university and industry speakers from the US and Europe whose research on plants and microbes spans the SynBio spectrum with respect both to technology and to applications, as well as short talks (selected from submitted abstracts) on cutting-edge topics. And second, to foster exchanges among public sector scientists and industry scientists, whose participation as poster presenters, exhibitors, and sponsors was actively encouraged because so much innovative SynBio work is being carried out at dynamic young companies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations

Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Amphiphile Organization in Organic Solutions: An Alternative Explanation for Small-Angle X-ray Scattering Features in Malonamide/Alkane Mixtures

In this work, the role of different intermolecular interactions in the aggregation of amphiphiles in an organic solvent is studied for systems of relevance to liquid-liquid extraction (LLE), a chemical process used to selectively recover metals from complex mixtures. Of specific interest is the role, or lack thereof, of hydrogen bonding, which is often assumed to be a main driver of the organic phase structural organization that has been linked to separation efficacy. Toward that end, a series of malonamide extractants in n-dodecane have been studied in the absence of any extracted aqueous solutes, including water. The series of extractants includes N,N'-dimethyl-N,N'-dibutyltetradecylmalonamide (DMDBTDMA), two of its homologs, and N,N'-dimethyl-N,N'-dioctylhexylethoxymalonamide (DMDOHEMA). This simplified model LLE system enables systematic investigation of the role of dipole-dipole and alkyl tail steric interactions in amphiphile aggregation. Small-angle X-ray scattering (SAXS) profiles computed from molecular dynamics trajectories are in good agreement with experimental SAXS data. Molecular dynamics simulations show that malonamide aggregation results from dipole-driven self-association and lacks characteristic aggregate sizes. Mid-q correlation peaks in the SAXS profiles emerge at high concentration for each malonamide. In those densely packed solutions, the correlation peaks are observed to result from alkyl tail-induced spacing between electron-rich polar head groups, with peak positions determined by the different alkyl tail lengths present in the malonamide molecule. This explanation of the SAXS correlation peaks contrasts with the prevailing literature, which attributes mesoscale features observed in small-angle scattering to the formation of microemulsions. Instead, this work finds that these features are present in the absence of water or any reverse micellar organization of the malonamides. As such, molecular-scale malonamide self-association and packing, rather than microemulsion-based colloidal-scale descriptions, is a more appropriate framework for these LLE systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Orographic enhancement of rainfall over the Congo Basin

Abstract The Congo rainforest located in central equatorial Africa is an important, yet understudied part of the globe surrounded by complex orographic features. A primitive understanding of precipitation processes such as mesoscale convective dynamics magnifies uncertainties in the future climate projections of the hydrological cycle over the Congo. Furthermore, the effects of orography, which is an important forcing for convection and precipitation, are poorly resolved by climate models, and ill‐conceptualized over the Congo. To address this knowledge gap, perturbed orographic forcing experiments are conducted using the Weather Research and Forecasting (WRF) mesoscale numerical model in a high‐resolution convection‐permitting model setup. The model simulated selected dates in November 2014. The thunderstorms and rainfall simulated in the control run for the case study analyzed in this article compared reasonably well to satellite‐derived brightness temperature and rainfall data. The results from this case study show that the dynamical impact of increasing the height of the East African Highlands is the blocking of the lower‐tropospheric tropical easterlies. This weakening of the lower‐tropospheric zonal winds increases the windshear over the Congo Basin resulting in slower propagating, more intense mesoscale convective systems with enhanced rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Molecular insights into the structure-property relationships of 3D printed polyamide reverse-osmosis membrane for desalination

3D-printing is an emerging method for manufacturing polyamide (PA) reserve osmosis (RO) membranes for water treatment and desalination, which can precisely control membrane structural properties, such as thickness, roughness, and resolution. However, the synthesis-structure (i.e., degree of cross-linking (DC), m-phenylenediamine/trimesoyl chloride (MPD/TMC) ratio, and membrane thickness) to property (permeability and water-salt selectivity) relationships for these membranes has not been well understood. At the same time, a microscopic understanding of the physical mechanism of water and salt transport is needed to guide the design of high-performance 3D-printed membranes and improve the printing efficiency. Furthermore, the atomic-scale transport features and energetics of water and salt ions are studied at high pressure for the 3D-printed PA RO membranes with the different DCs and MPD/TMC ratios through non-equilibrium molecular dynamics (NEMD) simulations. Factoring in membrane structure properties, rejection ratio of salt ions and pressure-dependent water flux, 3D-printed PA membranes having an MPD/TMC ratio of 3.0:2.0 and a DC between 80%~90% attains ideal performance: high water flux, high rejection of salt ions, and excellent structural integrity. Mechanistically, water permeability for highly cross-linked PA RO membranes depends on the temporary on-and-off channels that allow water molecules to jump from one cavity to another at high pressure. In addition, higher pressures cause rapid compaction of PA membranes’ free volume and membrane thickness. Membrane failure at high pressure is determined by the DC and MPD/TMC ratios-dependent compressive yield strength. In short, these findings provide physical insights for optimizing existing PA membranes and designing next-generation desalination membranes at the molecular level.

3D-printed PA RO membrane↗
DOE OSTI2025

Modelling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California

ABSTRACT Coastal wetlands, some of the most productive ecosystems on Earth, provide critical ecosystem services, including support of biodiversity, carbon sequestration and flood protection. In recent decades, these ecosystems have experienced extensive coastal wetland loss. Coastal wetland restoration provides a beacon of hope, offering a chance to reclaim these important habitats. However, even with billions of dollars invested worldwide in restoring coastal wetlands, we still lack comprehensive knowledge about the effectiveness of these restoration efforts in recovering wetland ecosystem functions and how future climate change may affect these efforts. The ability to evaluate how these ecosystems will function in the future is vital for examining current investments and developing future protection and management plans. We selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise (SLR) on coastal hydrology using the process‐based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site‐specific data. We designed a novel modelling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modelled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected SLR over the next few decades. The low‐lying Yampah Marsh, the reference site, is likely to be inundated due to future SLR when highest tides arrive, while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.

advanced terrestrial simulator↗
DOE OSTI2025

Effect of Solvents on Lignin–Surface Interactions via Molecular Dynamics Simulations

Lignin, an essential building block of lignocellulosic biomass, is a potential abundant source of aromatic monomers for the polymer and chemical industry. Reductive catalytic fractionation (RCF) is one promising process that can produce high yields of phenolic monomers and oligomers from lignin under different catalytic conditions. An important choice in optimizing RCF is the selection of solvent; however, detailed insights into solvent effects on lignin behaviors and interactions remain limited. Here, in this work, we perform all-atom molecular dynamics simulations to study the solvation of lignin, solvent-mediated conformational changes, and the interaction of solvated lignin oligomers with model surfaces. We focus on the behavior of an oligomeric lignin model compound in methanol, ethanol, a binary mixture of ethanol and water, and water at both the RCF reaction temperature (473 K) and room temperature. Analysis of structural features of lignin suggests that these three organic solvent systems favorably solvate lignin, resulting in a more extended conformation suitable for catalytic conversion to valuable chemicals. We further introduce model palladium (Pd) and carbon (C) surfaces to understand how solvent choice impacts adsorption onto a representative catalytic surface and support, and to quantify the competition among the reactant and solvent molecules for the surface. Unbiased simulations suggest strong adsorption of lignin on both Pd and C surfaces at 473 K, with notable solvent-mediated differences in adsorption energies. Additionally, our findings indicate that lignin adsorption is promoted by the entropy change resulting from the displacement of solvent molecules from the surface. This study provides a molecular perspective of adsorption of lignin onto varying surfaces, which is a step towards understanding and optimizing the catalytic conversion of lignin into valuable chemicals.

adsorption↗
DOE OSTI2020

Determination of latent dimensionality in international trade flow

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-negative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of non-negative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data. We then apply our method to decompose a network of international trade flows data from International Monetary Fund and shows that with a correct latent dimension determination, the resulting features are able to capture relevant empirical facts from economic literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-model files associated with the manuscript "Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California"

This package contains the data, simulation setups, notebooks and figures used in “Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California” (Xu et al., 2025). In this study, we selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise on coastal hydrology using the process-based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site-specific data. We designed a novel modeling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modeled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected sea level rise over the next few decades. The low-lying Yampah Marsh, the reference site, is likely to be inundated due to future sea level rise when highest tides arrive; while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.Several files can be found from this data package.1. README.md: This file describes the title, journal, co-authors, abstract, repository structure and model version.2. Simulation_Setups.zip: The file contains the model configuration files (XML format) for ATS. 3. Notebooks.zip: The file contains the Jupyter notebooks for generating the pre- and post-restoration meshes and the meshes of future scenarios. 4. Figures.zip: The file contains the figures used in the manuscript.5. Data.zip: The file contains the data used to drive the model simulations, including watershed and wetlands boundaries, mesh files and references to additional datasets (e.g., meteorological forcing, tidal dataset, DEMs, land cover, soil properties). Also, it contains water level observations at the restored wetland.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗