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Results for “structure of solids and liquids”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structure determination of ζ-N 2 from single-crystal X-ray diffraction and theoretical suggestion for the formation of amorphous nitrogen

The allotropy of solid molecular nitrogen is the consequence of a complex interplay between fundamental intermolecular as well as intramolecular interactions. Understanding the underlying physical mechanisms hinges on knowledge of the crystal structures of these molecular phases. That is especially true for ζ-N 2 , key to shed light on nitrogen’s polymerization. Here, we perform single-crystal X-ray diffraction on laser-heated N 2 samples at 54, 63, 70 and 86 GPa and solve and refine the hitherto unknown structure of ζ-N 2 . In its monoclinic unit cell (space group C 2/ c ), 16 N 2 molecules are arranged in a configuration similar to that of ε-N 2 . The structure model provides an explanation for the previously identified Raman and infrared lattice and vibrational modes of ζ-N 2 . Density functional theory calculations give an insight into the gradual delocalization of electronic density from intramolecular bonds to intermolecular space and suggest a possible pathway towards nitrogen’s polymerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Melting and defect transitions in FeO up to pressures of Earth’s core-mantle boundary

The high-pressure melting curve of FeO controls key aspects of Earth’s deep interior and the evolution of rocky planets more broadly. However, existing melting studies on wüstite were conducted across a limited pressure range and exhibit substantial disagreement. Here we use an in-situ dual-technique approach that combines a suite of >1000 x-ray diffraction and synchrotron Mössbauer measurements to report the melting curve for Fe 1-x O wüstite to pressures of Earth’s lowermost mantle. We further observe features in the data suggesting an order-disorder transition in the iron defect structure several hundred kelvin below melting. This solid-solid transition, suggested by decades of ambient pressure research, is detected across the full pressure range of the study (30 to 140 GPa). At 136 GPa, our results constrain a relatively high melting temperature of 4140 ± 110 K, which falls above recent temperature estimates for Earth’s present-day core-mantle boundary and supports the viability of solid FeO-rich structures at the roots of mantle plumes. The coincidence of the defect order-disorder transition with pressure-temperature conditions of Earth’s mantle base raises broad questions about its possible influence on key physical properties of the region, including rheology and conductivity.

58 GEOSCIENCES↗
DOE OSTI2024

High-entropy engineering of the crystal and electronic structures in a Dirac material

Dirac and Weyl semimetals are a central topic of contemporary condensed matter physics, and the discovery of new compounds with Dirac/Weyl electronic states is crucial to the advancement of topological materials and quantum technologies. Here we show a widely applicable strategy that uses high configuration entropy to engineer relativistic electronic states. We take the A MnSb 2 ( A = Ba, Sr, Ca, Eu, and Yb) Dirac material family as an example and demonstrate that mixing of Ba, Sr, Ca, Eu and Yb at the A site generates the compound (Ba 0.38 Sr 0.14 Ca 0.16 Eu 0.16 Yb 0.16 )MnSb 2 (denoted as A 5 MnSb 2 ), giving access to a polar structure with a space group that is not present in any of the parent compounds. A 5 MnSb 2 is an entropy-stabilized phase that preserves its linear band dispersion despite considerable lattice disorder. Although both A 5 MnSb 2 and A MnSb 2 have quasi-two-dimensional crystal structures, the two-dimensional Dirac states in the pristine A MnSb 2 evolve into a highly anisotropic quasi-three-dimensional Dirac state triggered by local structure distortions in the high-entropy phase, which is revealed by Shubnikov–de Haas oscillations measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Local structural distortions drive magnetic molecular field in compositionally complex spinel oxide

Understanding how local distortions determine the functional properties of high entropy materials, containing five or more elements at the same crystallographic site, is an open challenge. We address this for a compositionally complex spinel oxide (Mn 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 )Cr 2 O 4 (A 5 Cr 2 O 4 ). By comparatively examining extended X-ray absorption fine structure on A 5 Cr 2 O 4 and its parent counterparts, ACr 2 O 4 , along with density functional theory calculations for multiple configurations, we find that the element-specific distortions go beyond the first neighbor. Specifically, the strong Jahn-Teller distortion present in CuCr 2 O 4 is found to be completely suppressed in A 5 Cr 2 O 4 even locally. Instead, there is a broad distribution of Cu-O and Cu-Cr bond distances, while other A-O distances acquire certain specific values. This study demonstrates the additional flexibility of a cationic sublattice in maintaining a uniform long-range structure, in contrast to previous reports showing only the accommodative anionic sublattice. The mean-field magnetic interactions of A 5 Cr 2 O 4 exhibit a striking resemblance to those of NiCr 2 O 4 , despite the presence of multiple magnetic ions and variable bond lengths. This originates from the comparability of bond lengths around Cr in both materials. Our study paves the way for a deeper understanding of the impact of local structural distortions on the physical properties of compositionally complex quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dilute carbon in H3S under pressure

Abstract Recently, room temperature superconductivity was measured in a carbonaceous sulfur hydride material whose identity remains unknown. Herein, first-principles calculations are performed to provide a chemical basis for structural candidates derived by doping H 3 S with low levels of carbon. Pressure stabilizes unusual bonding configurations about the carbon atoms, which can be six-fold coordinated as CH 6 entities within the cubic H 3 S framework, or four-fold coordinated as methane intercalated into the H-S lattice, with or without an additional hydrogen in the framework. The doping breaks degenerate bands, lowering the density of states at the Fermi level ( N F ), and localizing electrons in C-H bonds. Low levels of CH 4 doping do not increase N F to values as high as those calculated for $$Im\bar{3}m$$ I m 3 ¯ m -H 3 S, but they can yield a larger logarithmic average phonon frequency, and an electron–phonon coupling parameter comparable to that of R 3 m -H 3 S. The implications of carbon doping on the superconducting properties are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine learning-based discovery of vibrationally stable materials

The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning potential assisted exploration of complex defect potential energy surfaces

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Spin and lattice dynamics of the two-dimensional van der Waals ferromagnet CrI3

Chromium tri-iodide (CrI 3 ) is a prototypical ferromagnetic van der Waals insulator with its genuinely two-dimensional (2D) long-range magnetic order below 45 K demonstrated recently. The underlying magnetic anisotropy has not been completely understood while both the Dzyaloshinskii-Moriya (DM) interaction and the Kitaev—Γ type interaction have been considered as the relevant magnetic Hamiltonian. In addition, the relation between the crystal structure and the magnetic order needs to be further elucidated concerning their possible coupling in few-layer samples and in the topmost surface layers of bulk samples. Here, we investigate these issues via temperature- and magnetic field-dependent terahertz spectroscopy on bulk CrI 3 single crystals, focusing on the dynamics of ferromagnetic resonances (FMRs) and optical phonons in the terahertz (THz) region from 4 to 120 cm -1 (from 0.5 to 15 meV). We narrow down the possible ranges of the interaction parameters such as the off-diagonal symmetric terms and the single-ion anisotropy. The accurate values of these parameters significantly constrain the magnitude of possible Kitaev—Γ exchange interaction and the topological magnon gap. Moreover, the structure-magnetism relationship was critically analyzed based on the temperature- and field-dependences of two E u in-plane optical phonon modes, which shows that the commonly believed structural phase diagram of CrI 3 , derived from surface-preferential data, has to be seriously modified.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Visualization of dynamic polaronic strain fields in hybrid lead halide perovskites

Excitation localization involving dynamic nanoscale distortions is a central aspect of photocatalysis, quantum materials and molecular optoelectronics. Experimental characterization of such distortions requires techniques sensitive to the formation of point-defect-like local structural rearrangements in real time. Here, we visualize excitation-induced strain fields in a prototypical member of the lead halide perovskites via femtosecond resolution diffuse X-ray scattering measurements. Here, this enables momentum-resolved phonon spectroscopy of the locally distorted structure and reveals radially expanding nanometre-scale strain fields associated with the formation and relaxation of polarons in photoexcited perovskites. Quantitative estimates of the magnitude and shape of this polaronic distortion are obtained, providing direct insights into the dynamic structural distortions that occur in these materials. Optical pump–probe reflection spectroscopy corroborates these results and shows how these large polaronic distortions transiently modify the carrier effective mass, providing a unified picture of the coupled structural and electronic dynamics that underlie the optoelectronic functionality of the hybrid perovskites.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Imaging moiré flat bands in three-dimensional reconstructed WSe 2 /WS 2 superlattices

Moiré superlattices in transition metal dichalcogenide (TMD) heterostructures can host novel correlated quantum phenomena due to the interplay of narrow moiré flat bands and strong, long-range Coulomb interactions. However, microscopic knowledge of the atomically reconstructed moiré superlattice and resulting flat bands is still lacking, which is critical for fundamental understanding and control of the correlated moiré phenomena. Here we quantitatively study the moiré flat bands in three-dimensional (3D) reconstructed WSe 2 /WS 2 moiré superlattices by comparing scanning tunnelling spectroscopy (STS) of high-quality exfoliated TMD heterostructure devices with ab initio simulations of TMD moiré superlattices. A strong 3D buckling reconstruction accompanied by large in-plane strain redistribution is identified in our WSe 2 /WS 2 moiré heterostructures. STS imaging demonstrates that this results in a remarkably narrow and highly localized K-point moiré flat band at the valence band edge of the heterostructure. A series of moiré flat bands are observed at different energies that exhibit varying degrees of localization. Our observations contradict previous simplified theoretical models but agree quantitatively with ab initio simulations that fully capture the 3D structural reconstruction. Furthermore, our results reveal that the strain redistribution and 3D buckling in TMD heterostructures dominate the effective moiré potential and the corresponding moiré flat bands at the Brillouin zone K points.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced superconductivity and ferroelectric quantum criticality in plastically deformed strontium titanate

The properties of quantum materials are commonly tuned using experimental variables such as pressure, magnetic field and doping. Here we explore a different approach using irreversible, plastic deformation of single crystals. We show that compressive plastic deformation induces low-dimensional superconductivity well above the superconducting transition temperature (Tc) of undeformed SrTiO3, with evidence of possible superconducting correlations at temperatures two orders of magnitude above the bulk Tc. The enhanced superconductivity is correlated with the appearance of self-organized dislocation structures, as revealed by diffuse neutron and X-ray scattering. We also observe deformation-induced signatures of quantum-critical ferroelectric fluctuations and inhomogeneous ferroelectric order using Raman scattering. Our results suggest that strain surrounding the self-organized dislocation structures induces local ferroelectricity and quantum-critical dynamics that strongly influence Tc, consistent with a theory of superconductivity enhanced by soft polar fluctuations. Our results demonstrate the potential of plastic deformation and dislocation engineering for the manipulation of electronic properties of quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Observation of spin-momentum locked surface states in amorphous Bi 2 Se 3

Crystalline symmetries have played a central role in the identification and understanding of quantum materials. Here we investigate whether an amorphous analogue of a well-known three-dimensional strong topological insulator has topological properties in the solid state. We show that amorphous Bi 2 Se 3 thin films host a number of two-dimensional surface conduction channels. Our angle-resolved photoemission spectroscopy data are consistent with a dispersive two-dimensional surface state that crosses the bulk gap. Spin-resolved photoemission spectroscopy shows this state has an anti-symmetric spin texture, confirming the existence of spin-momentum locked surface states. We discuss these experimental results in light of theoretical photoemission spectra obtained with an amorphous topological insulator tight-binding model, contrasting it with alternative explanations. In conclusion, the discovery of spin-momentum locked surface states in amorphous materials opens a new avenue to characterize amorphous matter and triggers the search for an overlooked subset of quantum materials outside of current classification schemes.

36 MATERIALS SCIENCE↗