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At least 667 records · Page 37

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DOE OSTI2021

Topological Nernst effect, anomalous Nernst effect, and anomalous thermal Hall effect in the Dirac semimetal Fe 3 Sn 2

Topological semimetals have been a focal point of research in recent years. One of the salient characters of topological semimetals is the anomalous Hall effect arising from the nontrivial momentum-space Berry phase. In contrast, anomalous transverse thermoelectric coefficient and thermal Hall effect are much less explored up to date. In this paper, we report thermoelectric and thermal transport properties of Fe 3 Sn 2 , a prototypical Dirac semimetal. We find that Fe 3 Sn 2 exhibits large topological Nernst effect which is associated with the non-zero spin chirality of the skyrmion bubble phase. Furthermore, Fe 3 Sn 2 shows prominent anomalous thermal Hall effect and anomalous Nernst effect with features similar to the anomalous Hall effect. The anomalous Nernst signal is about 2.1 μV/K at room temperature, which is comparable to the largest value reported thus far. These results highlight the synergic effects of Berry phase in both real space and momentum space of Fe 3 Sn 2 , and this study demonstrates an effective strategy of investigating topological materials by measuring their transverse thermal and thermoelectric responses.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

58 GEOSCIENCES↗

DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Near-Quantitative Predictions of the First-Shell Coordination Structure of Hydrated First-Row Transition Metal Ions Using K-Edge X-ray Absorption Near-Edge Spectroscopy

Understanding the solvation structure of transition metal ions is important due to its broad impact on the kinetics, stability, and reactivity in a variety of applications in geochemistry, biochemistry, energy storage, and environmental chemistry. Using water as the common ligand, we study the X-ray absorption pre-edge and near-edge spectra at the K-edge of a near-complete series of hydrated first-row transition metal ions with d-orbital occupancy ranging from d 2 to d 10 . Starting with optimized structures that were derived from an explicit solvation treatment at the density functional theory (DFT) level, we then compute the pre-edge X-ray absorption spectra at the metal ion K-edge at the time dependent density functional theory (TDDFT) and restricted active-space second-order perturbation theory (RASPT2) levels of theory. TDDFT calculations provide accurate results for spectra that are dominated by single excitations, while significant improvements were obtained with RASPT2 calculations that correctly distinguish between singly and doubly excited states, where relevant, with quantitative accuracy compared with experiment. We analyze and assign the dierent pre-edge features for each metal ion in order to reveal the impact of the variations in the d orbital occupancy on the rst-shell coordination environment. For completeness, we also report the lowest energy ligand-field d-d transitions for all the transition metal aqua ions considered in this study using complete active space second order perturbation theory (CASPT2).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗

DOE OSTI2021

Quasi-One-Dimensional Structure and Possible Helical Antiferromagnetism of RbMn 6 Bi 5

Quasi-one-dimensional materials exhibit not only unique crystal structure but also abundant physical properties such as charge density wave, Luttinger liquid, and superconductivity. In this study, we report the discovery, structure, and physical properties of a new manganese-based quasi-one-dimensional material RbMn 6 Bi 5 , which crystallizes in a monoclinic space group C 2/ m (No. 12) with lattice parameters a = 23.286(5) Å, b = 4.6215(9) Å, c = 13.631(3) Å, and β = 125.00(3)°. The structure features [Mn 6 Bi 5 ] –1 double-walled column extending along the [010] direction, together with Bi–Bi homoatomic bonds linking the columns and the countercation Rb + . The temperature-dependent resistivity clearly indicates a significant resistivity anisotropy for RbMn 6 Bi 5 , whereas the magnetic susceptibility and specific heat measurements show that RbMn 6 Bi 5 is antiferromagnetic below 82 K. The density functional theory calculations indicate that RbMn 6 Bi 5 is a quasi-one-dimensional metal with possible helical antiferromagnetic configuration. The discovery of RbMn 6 Bi 5 confirms the viability of discovering new quasi-one-dimensional materials in manganese-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Spin and Orbital Effects on Asymmetric Exchange Interaction in Polar Magnets: M(IO 3 ) 2 (M = Cu and Mn)

We report magnetic polar materials feature an astonishing range of physical properties, such as magnetoelectric coupling, chiral spin textures, and related new spin topology physics. This is primarily attributable to their lack of space inversion symmetry in conjunction with unpaired electrons, potentially facilitating an asymmetric Dzyaloshinskii–Moriya (DM) exchange interaction supported by spin–orbital and electron–lattice coupling. However, engineering the appropriate ensemble of coupled degrees of freedom necessary for enhanced DM exchange has remained elusive for polar magnets. Here, we study how spin and orbital components influence the capability of promoting the magnetic interaction by studying two magnetic polar materials, α-Cu(IO 3 ) 2 ( 2 D) and Mn(IO 3 ) 2 ( 6 S), and connecting their electronic and magnetic properties with their structures. The chemically controlled low-temperature synthesis of these complexes resulted in pure polycrystalline samples, providing a viable pathway to prepare bulk forms of transition-metal iodates. Rietveld refinements of the powder synchrotron X-ray diffraction data reveal that these materials exhibit different crystal structures but crystallize in the same polar and chiral P2 1 space group, giving rise to an electric polarization along the b-axis direction. The presence and absence of an evident phase transition to a possible topologically distinct state observed in α-Cu(IO 3 ) 2 and Mn(IO 3 ) 2 , respectively, imply the important role of spin–orbit coupling. Neutron diffraction experiments reveal helpful insights into the magnetic ground state of these materials. While the long-wavelength incommensurability of α-Cu(IO 3 ) 2 is in harmony with sizable asymmetric DM interaction and low dimensionality of the electronic structure, the commensurate stripe AFM ground state of Mn(IO 3 ) 2 is attributed to negligible DM exchange and isotropic orbital overlapping. The work demonstrates connections between combined spin and orbital effects, magnetic coupling dimensionality, and DM exchange, providing a worthwhile approach for tuning asymmetric interaction, which promotes evolution of topologically distinct spin phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

LiMo 8 O 10 : Polar Crystal Structure with Infinite Edge-Sharing Molybdenum Octahedra

Here, polycrystalline LiMo 8 O 10 was prepared in a sealed Mo crucible at 1380 °C for 48 h using the conventional high-temperature solid-state method. The polar tetragonal crystal structure (space group I4 1 md) is confirmed based on the Rietveld refinement of powder neutron diffraction and 7 Li/ 6 Li solid-state NMR. The crystal structure features infinite chains of Mo 4 O 5 (i.e., Mo 2 Mo 4/2 O 6/2 O 6/3 ) as a repeat unit containing edge-sharing Mo 6 octahedra with strong Mo–Mo metal bonding along the chain. X-ray absorption near-edge spectroscopy of the Mo-L 3 edge is consistent with the formal Mo valence/configuration. Magnetic measurements reveal that LiMo 8 O 10 is paramagnetic down to 1.8 K. Temperature-dependent resistivity [ρ(T)] measurement indicates a semiconducting behavior that can be fitted with Mott’s variable range hopping conduction mechanism in the temperature range of 215 and 45 K. The ρ(T) curve exhibits an exponential increase below 5 K with a large ratio of ρ 1.8 /ρ 300 = 435. LiMo 8 O 10 shows a negative field-dependent magnetoresistance between 2 and 25 K. Heat capacity measurement fitted with the modified Debye model yields the Debye temperature of 365 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features—such as complex symmetries and the requirement of exactness guarantees—that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. Here, the design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

Sharma, Bharat↗

DOE OSTI2023

Reading Between the Lines: Measuring the Effects of Linguistic-Based Indicators of Deception on Experts’ Identification and Categorization of Disinformation

There is currently very limited research into how experts analyze and assess potentially fraudulent content in their expertise areas, and most research within the disinformation space involves very limited text samples (e.g., news headlines). The overarching goal of the present study was to explore how an individual’s psychological profile and the linguistic features in text might influence an expert’s ability to discern disinformation/fraudulent content in academic journal articles. At a high level, the current design tasked experts with reading journal articles from their area of expertise and indicating if they thought an article was deceptive or not. Half the articles they read were journal papers that had been retracted due to academic fraud. Demographic and psychological inventory data collected on the participants was combined with performance data to generate insights about individual expert susceptibility to deception. Our data show that our population of experts were unable to reliably detect deception in formal technical writing. Several psychological dimensions such as comfort with uncertainty and intellectual humility may provide some protection against deception. This work informs our understanding of expert susceptibility to potentially fraudulent content within official, technical information and can be used to inform future mitigative efforts and provide a building block for future disinformation work.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Ba5Sb8: The Highest Homologue of the Family of Binary Semiconducting Barium Antimonides BanSb2n−2 (n ≥ 2)

A novel binary compound within the Ba–Sb phase diagram, Ba5Sb8, was synthesized by combining elements with an excess of Sb in an alumina crucible. Structural elucidation was performed using single-crystal X-ray diffraction. This compound crystallizes in the orthorhombic space group Fdd2 with unit cell parameters of a = 15.6568(13) Å, b = 35.240(3) Å, c = 6.8189(6) Å, adopting its own structure type. The most distinctive features of the structure are the eight-membered [Sb8]10− polyanionic fragments which have no known precedents among antimonides. They are separated by five Ba2+ cations, which afford the charge balance and enable adherence to the Zintl–Klemm formalism. Ba5Sb8 is the highest known member of the homologous series within the family of barium antimonides BanSb2n−2 (n ≥ 2), all of which boast anionic substructures with oligomeric moieties of pnictogen atoms with varied lengths and topologies. Electronic structure calculations indicate an indirect narrow bandgap of ca. 0.45 eV, which corroborates the valence-precise chemical bonding in Ba5Sb8.

Samarakoon, S. M. Gayomi K.↗

DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Version 2.0.0 - M-SPARC: Matlab-Simulation Package for Ab-initio Real-space Calculations

M-SPARC is a Matlab code for performing ab initio Kohn–Sham Density Functional Theory simulations. Version 2.0.0 of the software further extends its capability to include relativistic effects, dispersion interactions, and advanced semilocal/nonlocal exchange–correlation functionals. These features significantly increase the fidelity of first principles calculations that can be performed using M-SPARC.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self-assembled heterogeneous monolayer films of doped-semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine-learning-based analyses of hyperspectral EELS datasets are demonstrated. Additionally, in the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non-linear autoencoders are used to create a highly-reduced latent-space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.

Wang, Song↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Wang, Song↗

DOE OSTI2023

Multiscale graph neural network autoencoders for interpretable scientific machine learning

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. Further, this reduction procedure essentially amounts to flowfieldconditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.

97 MATHEMATICS AND COMPUTING↗