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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics-based adaptivity of a spectral method for the Vlasov–Poisson equations based on the asymmetrically-weighted Hermite expansion in velocity space

We propose a spectral method for the 1D-1V Vlasov–Poisson system where the discretization in velocity space is based on asymmetrically-weighted Hermite functions, dynamically adapted via a scaling α and shifting u of the velocity variable. Specifically, at each time instant an adaptivity criterion selects new values of α and u based on the numerical solution of the discrete Vlasov–Poisson system obtained at that time step. Once the new values of the Hermite parameters α and u are fixed, the Hermite expansion is updated and the discrete system is further evolved for the next time step. The procedure is applied iteratively over the desired temporal interval. The key aspects of the adaptive algorithm are: the map between approximation spaces associated with different values of the Hermite parameters that preserves total mass, momentum and energy; and the adaptivity criterion to update α and u based on physics considerations relating the Hermite parameters to the average velocity and temperature of each plasma species. For the discretization of the spatial coordinate, we rely on Fourier functions and use the implicit midpoint rule for time stepping. The resulting numerical method possesses intrinsically the property of fluid-kinetic coupling, where the low-order terms of the expansion are akin to the fluid moments of a macroscopic description of the plasma, while kinetic physics is retained by adding more spectral terms. Moreover, the scheme features conservation of total mass, momentum and energy associated in the discrete, for periodic boundary conditions. A set of numerical experiments confirms that the adaptive method outperforms the non-adaptive one in terms of accuracy and stability of the numerical solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geometric entropies and their Hamiltonian flows

In holographic theories, the Hubeny-Rangamani-Takayanagi (HRT) area operator plays a key role in our understanding of the emergence of semiclassical Einstein-Hilbert gravity. When higher derivative corrections are included, the role of the area is instead played by a more general functional known as the geometric entropy. It is thus of interest to understand the flow generated by the geometric entropy on the classical phase space. In particular, the fact that the associated flow in Einstein-Hilbert or Jackiw-Teitelboim (JT) gravity induces a relative boost between the left and right entanglement wedges is deeply related to the fact that gravitational dressing promotes the von Neumann algebra of local fields in each wedge to type II. This relative boost is known as a boundary-condition-preserving (BCP) kink-transformation. In a general theory of gravity (with arbitrary higher-derivative terms), it is straightforward to show that the flow continues to take the above geometric form when acting on a spacetime where the HRT surface is the bifurcation surface of a Killing horizon. However, the form of the flow on other spacetimes is less clear. In this paper, we use the manifestly-covariant Peierls bracket to explore such flows in two-dimensional theories of JT gravity coupled to matter fields with higher derivative interactions. The results no longer take a purely geometric form and, instead, demonstrate new features that should be expected of such flows in general higher derivative theories. We also show how to obtain the above flows using Poisson brackets.

AdS-CFT correspondence↗
DOE OSTI2025

A microstructural signature of the coesite-quartz transformation: New insights from high-pressure experiments and EBSD

Ultra-high pressure (UHP) metamorphism is difficult to identify in continental crust as few petrological barometers are suitable for dominantly felsic lithologies. In such cases, burial to extreme depths is commonly identified through the preservation of coesite, a high-pressure polymorph of SiO 2 that typically forms at depths exceeding ∼ 100 km (i.e., > 2 GPa pressure). Unfortunately, coesite readily transforms to quartz upon exhumation, meaning that UHP terranes may often be overlooked. While some studies have suggested that quartz may inherit an orientation signature indicative of former coesite, both the specific nature of this signature and the conditions favouring its development remain uncertain. Here, to address this problem, we combine electron backscatter diffraction analysis of natural and experimental samples to explore microstructural evolution across the coesite-quartz phase transformation. We demonstrate that neighbouring domains of quartz commonly feature an 84 ± 4° rotation of [c] axes around the pole of a common {m} plane. This orientation relationship is a product of epitaxy, whereby the {$11\bar{2}2$} Japan twin plane in quartz nucleates on the (010) plane in coesite. In supercell simulations, the nucleation of Japan twins can be explained by the energetically favourable alignment of quartz tetrahedra on parental coesite tetrahedra. Through experiments, we demonstrate that this signature emerges over a broad range of conditions, regardless of the availability of nucleation sites (e.g., grain boundaries) or the density of crystal lattice defects (e.g., dislocations). Overall, our work provides a quantitative and unambiguous tool for identifying UHP terranes from quartz in isolation.

Coesite↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Plastic recognition and electrogenic uniport translocation of 1 st -, 2 nd -, and 3 rd -row transition and post-transition metals by primary-active transmembrane P 1B-2 -type ATPase pumps

Transmembrane P 1B -type ATPase pumps catalyze the extrusion of transition metal ions across cellular lipid membranes to maintain essential cellular metal homeostasis and detoxify toxic metals. Zn(II)-pumps of the P 1B-2 -type subclass, in addition to Zn 2+ , select diverse metals (Pb 2+ , Cd 2+ and Hg 2+ ) at their transmembrane binding site and feature promiscuous metal-dependent ATP hydrolysis in the presence of these metals. Yet, a comprehensive understanding of the transport of these metals, their relative translocation rates, and transport mechanism remain elusive. We developed a platform for the characterization of primary-active Zn(II)-pumps in proteoliposomes to study metal selectivity, translocation events and transport mechanism in real-time, employing a “multi-probe” approach with fluorescent sensors responsive to diverse stimuli (metals, pH and membrane potential). Together with atomic-resolution investigation of cargo selection by X-ray absorption spectroscopy (XAS), we demonstrate that Zn(II)-pumps are electrogenic uniporters that preserve the transport mechanism with 1 st -, 2 nd - and 3 rd -row transition metal substrates. Promiscuous coordination plasticity, guarantees diverse, yet defined, cargo selectivity coupled to their translocation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗
DOE OSTI2021

Targeted disruption of pi–pi stacking in Malaysian banana lectin reduces mitogenicity while preserving antiviral activity

Lectins, carbohydrate-binding proteins, have been regarded as potential antiviral agents, as some can bind glycans on viral surface glycoproteins and inactivate their functions. However, clinical development of lectins has been stalled by the mitogenicity of many of these proteins, which is the ability to stimulate deleterious proliferation, especially of immune cells. We previously demonstrated that the mitogenic and antiviral activities of a lectin (banana lectin, BanLec) can be separated via a single amino acid mutation, histidine to threonine at position 84 (H84T), within the third Greek key. The resulting lectin, H84T BanLec, is virtually non-mitogenic but retains antiviral activity. Decreased mitogenicity was associated with disruption of pi–pi stacking between two aromatic amino acids. To examine whether we could provide further proof-of-principle of the ability to separate these two distinct lectin functions, we identified another lectin, Malaysian banana lectin (Malay BanLec), with similar structural features as BanLec, including pi–pi stacking, but with only 63% amino acid identity, and showed that it is both mitogenic and potently antiviral. We then engineered an F84T mutation expected to disrupt pi–pi stacking, analogous to H84T. As predicted, F84T Malay BanLec (F84T) was less mitogenic than wild type. However, F84T maintained strong antiviral activity and inhibited replication of HIV, Ebola, and other viruses. The F84T mutation disrupted pi–pi stacking without disrupting the overall lectin structure. These findings show that pi–pi stacking in the third Greek key is a conserved mitogenic motif in these two jacalin-related lectins BanLec and Malay BanLec, and further highlight the potential to rationally engineer antiviral lectins for therapeutic purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ferromagnetic Exchange and Slow Magnetic Relaxation in Cobalt Bis(1,2-dithiolene)-Bridged Dilanthanide Complexes

The construction of multinuclear lanthanide-based molecules with significant magnetic exchange interactions represents a key challenge in the realization of single-molecule magnets with high operating temperatures. Here, we report the synthesis and magnetic characterization of two series of heterobimetallic compounds, (Cp* 2 Ln) 2 (μ-Co(pdt) 2 ) (Ln = Y 3+ , Gd 3+ , Dy 3+ ; pdt 2– = 1,2-diphenylethylenedithiolate) and [K(18-crown-6)][(Cp* 2 Ln) 2 (μ-Co(pdt) 2 )] (Ln = Y 3+ , Gd 3+ ), featuring two lanthanide centers bridged by a cobalt bis(1,2-dithiolene) complex. Dc magnetic susceptibility data collected for the Gd congeners indicate significant Gd–Co ferromagnetic exchange interactions with fits affording J = +11.5 and +7.33 cm –1 , respectively. Magnetization decay and ac magnetic susceptibility measurements carried out on the single-molecule magnet (Cp* 2 Dy) 2 (μ-Co(pdt) 2 ) reveal full suppression of quantum tunneling and open-loop hysteresis persisting up to 3.5 K. These results, along with those of high-field EPR spectroscopy, suggest that transition metalloligands can enforce strong exchange interactions with adjacent lanthanide centers while maintaining a geometry that preserves molecular anisotropy. Furthermore, the magnetic properties of [K(18-crown-6)][(Cp* 2 Gd) 2 (μ-Co(pdt) 2 )] show that increasing the spin of the ground state of the bridging complex may be a viable alternative to increasing J in obtaining well-isolated, strongly coupled magnetic ground states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ray-tracing image simulations of transparent objects with complex shape and inhomogeneous refractive index

Optical images of transparent three-dimensional objects can be different from a replica of the object’s cross section in the image plane, due to refraction at the surface or in the body of the object. Simulations of the object’s image are thus needed for the visualization and validation of physical models. We report ray tracing image simulations that achieved high physical fidelity, reproducing optical behaviors and image features not rendered in previous studies. We replicated brightfield microscopy images of drops with complex shapes, and images of pressure and shock waves traveling inside them. For high physical fidelity, the simulations must replicate the spatial and angular distribution of illumination rays, and both the experiment and the simulation must be designed for accurate optical modeling. The simulations are highly sensitive to the properties of the drops and can be used to diagnose and refine fluid dynamics models. The simulated images can also be optimized to extract multiple 3D properties from experimental images. Compared to specialized single-shot 3D imaging methods, this approach has the advantage that it preserves the experimental simplicity, the high resolution, and the visual interpretability characteristic to basic optical imaging. The techniques introduced here are directly applicable to optical microscopy, so they can be used in other fields, such as microfluidics and biology, to expand the type and the accuracy of three-dimensional information that can be extracted from basic optical images.

Cavitation↗
DOE OSTI2023

The Oxidation State of Sulfur in Apatite of Martian Meteorite—Shergotty

Apatite can incorporate sulfur in its reduced form (S 2− ) when apatite equilibrates with a silicate melt under reducing conditions. Incorporation of sulfate (S 6+ ) has been observed in terrestrial apatite under oxidizing conditions. Thus, it has been suggested that the proportions of S 6+ /S 2− in apatite may record the oxygen fugacity ( f O 2 ) during the formation and/or equilibration of apatite grains with a silicate melt in a wide variety of igneous and metamorphic rocks, including from Earth, Mars, the Moon, and in materials from the asteroid belt. Martian rocks, which record f O 2 values intermediate between those recorded by rocks from the Moon and Earth, may have apatite that contains only S 2− or mixtures of S 6+ and S 2− . Here, we present new measurements of the oxidation state of sulfur in apatite grains in the basaltic shergottite, Shergotty, which exhibits spectral features consistent with the presence of sulfide (S 2− ) structurally bound in apatite, and no evidence for the presence of sulfite (S 4+ ) or sulfate (S 6+ ). Further, the presence of sulfide-only apatite in Shergotty is consistent with other mineralogical records of f O 2 in this meteorite, which are calculated from other late-stage crystallizing phases like Fe-Ti oxides as well as from early crystallizing phases like clinopyroxene ( D Eu Cpx/melt ) of ΔIW + 1.9 to ΔIW + 3.5. At these f O 2 values, S is present in silicate melts as only S 2− , and this suggests that the oxidation state of sulfur records and preserves the f O 2 during the igneous crystallization of apatite reinforcing the idea that sulfur in apatite can be used as an igneous oxybarometer.

58 GEOSCIENCES↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluation of Data Catalog Software for Hanford Site Environmental Datasets

Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrable symplectic maps with a polygon tessellation

Identifying integrable dynamics remains a formidable challenge, and despite centuries of research, only a handful of examples are known to date. In this article, we explore a distinct form of area-preserving (symplectic) mappings derived from the stroboscopic Poincaré cross section of a kicked rotator—an oscillator subjected to an external force periodically switched on in short pulses. The significance of this class of problems extends to various applications in physics and mathematics, including particle accelerators, crystallography, and studies of chaos. Notably, Suris's theorem constrains the integrability within this category of mappings, outlining potential scenarios with analytic invariants of motion. In this paper, we challenge the assumption of the analyticity of the invariant by exploring piecewise linear transformations on a torus ( T 2 ) and associated systems on the plane ( R 2 ), incorporating arithmetic quasiperiodicity and discontinuities. Introducing a new automated technique, we discovered previously unknown scenarios featuring polygonal invariants that form perfect tessellations and, moreover, fibrations of the plane or torus. This work reveals a novel category of planar tilings characterized by discrete symmetries that emerge from the invertibility of transformations and are intrinsically linked to the presence of integrability. Our algorithm relies on the analysis of the Poincaré rotation number and its piecewise monotonic nature for integrable cases, contrasting with the noisy behavior in the case of chaos, thereby allowing for clear separation. Some of the newly discovered systems exhibit the peculiar behavior of “integrable diffusion,” characterized by infinite and quasirandom hopping between tiles while being confined to a set of invariant segments. Finally, through the implementation of a smoothening procedure, all mappings can be generalized to quasi-integrable scenarios with suppressed volume occupied by chaotic trajectories, thereby opening doors to potential practical applications. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗
DOE OSTI2026

Temporal entanglement transitions in the periodically driven Ising chain

Periodically driven quantum systems can host non-equilibrium phenomena without static analogs, including in their entanglement dynamics. Here, we discover (TET) in a Floquet spin chain, which correspond to a quantum phase transition in the spectrum of the entanglement Hamiltonian and are signaled by dynamical spontaneous symmetry breaking. We identify the symmetry principles underlying these transitions: they appear when the driven Hamiltonian preserves global symmetry (here, ℤ2), the initial state respects this symmetry, and the reduced density matrix carries weight in both subsystem-parity sectors, with TET occurring precisely when the sector weights become equal (given the previous two conditions are also satisfied). Intriguingly, we find these transitions across a broad range of driving frequencies (from adiabatic to high-frequency regime) and independently of drive details, where they manifest as periodic, sharp entanglement spectrum reorganizations marked by the Schmidt-gap closure, a vanishing entanglement echo, and symmetry-quantum-number flips, while remaining invisible to conventional local observables. At high frequencies, the entanglement Hamiltonian acquires an intrinsic timescale decoupled from the drive period, rendering the transitions genuine steady-state features. Finite-size scaling reveals universal critical behavior with correlation-length exponent 𝜈 = 1, matching equilibrium Ising universality despite its emergence from purely dynamical mechanisms decoupled from static criticality. Furthermore, our work establishes TET as novel features in Floquet quantum matter.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Advanced User Interface Capabilities [Slides]

This tutorial will review the data plotting and geometry visualization capabilities in the Fulcrum user interface. This tutorial will help you become familiar with Fulcrum’s 2D plot, and 2D and 3D geometry visualization features. You will learn how to identify plottable data items, compose and export plot and plot data for SCALE plot formats (SDF, Ampx MG/CE, PLT, F71, PTP, SPF, ORIGEN Gamma data, etc.) and visualize, navigate, cut, hide, and export the geometry and spatial data (fission-, dose-map, etc.) overlays in 2D and 3D. No prior experience with SCALE is required. Attendees can follow along using 6.3.0-beta.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Frequent Fulcrum Functions: The Basics of SCALE’s Graphical User Interface [Slides]

This tutorial introduces the Fulcrum graphical user interface and the basic functions that enhance the common activities of creating, editing, navigating, executing, and visualizing SCALE input files. This tutorial will help you become familiar with the Fulcrum input file text editor and the integrated input development environment features of autocompletion, automatic checking, cursor context, and input navigation. In addition, the Fulcrum and SCALE runtime environment will be reviewed to improve the understanding of job execution workflow. This tutorial does not cover data and geometry plotting. Please see the Advanced User Interface Capabilities tutorial for details regarding plotting data and geometry. No prior experience with SCALE is required. You can follow along using SCALE 6.2 or 6.3-beta.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗