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At least 307 records · Page 17

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DOE OSTI2026

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

battery↗

DOE OSTI2026

Hyperfine spectroscopy and laser cooling of the fermionic isotopes 47 Ti and 49 Ti

Here, we report on magneto-optical trapping of the two fermionic isotopes of atomic titanium, 47 Ti and 49 Ti . Unlike the even mass-number isotopes, which were recently laser cooled, 47 Ti and 49 Ti have nonzero nuclear spins and, consequently, their atomic levels are split by hyperfine structure. Combining and comparing theoretical calculations and atomic beam-spectroscopy measurements, we determine the hyperfine structures and isotope shifts of the 3⁢𝑑 2 ⁢4⁢𝑠 2 𝑎 3 ⁢𝐹 4 → 3⁢𝑑 2 ⁢( 3 𝑃)⁢4⁢𝑠⁢4⁢𝑝⁢( 3 𝑃 𝑜 ) 𝑦 5 ⁢𝐷$^o_4$ optical-pumping transition at optical wavelength 391 nm and the 3⁢𝑑 3 ⁢(4𝐹)⁢4⁢𝑠 𝑎 5 ⁢𝐹 5 → 3⁢𝑑 3 ⁢( 4 𝐹)⁢4⁢𝑝 𝑦 5 ⁢𝐺$^𝑜_6$ laser-cooling transition at wavelength 498 nm. With this information, we produce magneto-optical traps of both 47 Ti and 49 Ti by applying two additional tones of light to repump atoms to the maximum-spin states on the laser-cooling transition. Directly loading from the atomic flux of a titanium sublimation pump, we produce 47 Ti and 49 Ti traps with 731(190) and 1142(240) atoms, and with lifetimes of 330⁢(15) and 310⁢(8)⁢ ms, respectively.

atomic spectra↗

DOE OSTI2026

Structural, Electronic, and Photophysical Insights into a Few Atom Copper-Sulfur Cluster in the Solid and Solution States

Coinage-metal chalcogenide clusters are widely studied for their attractive photoluminescence properties. Copper chalcogenides are especially promising, but are often confined to solid-state investigations due to their limited solution stability and the difficulty of synthesizing stable, well-defined clusters. Here, we investigate copper–sulfur clusters incorporating a small number of Cu atoms to elucidate fundamental atomic interactions, ground- and excited-state characteristics, and photophysical behavior in both solid and solution. We have synthesized the Cu6(4,6-dimethyl-2-mercaptopyrimidine)6 cluster in both neutral and charged states, Cu6 and Cu6-2H2+, respectively, by selective ligand protonation. The molecular structures are determined using single-crystal X-ray diffraction, while Cu K-edge X-ray absorption spectroscopy is used to probe Cu electronic structure differences arising from the ligand modification. Steady-state and pump-probe optical spectroscopy is used to investigate photophysical properties, interpreted using density functional theory methods. Both clusters exhibit good stability in the solid state and in solution and show characteristic near-infrared emission with microsecond lifetimes. Overall, the Cu6S6 clusters display favorable charge–transfer characteristics and show potential for further use in driving photochemical transformations.

Copper-sulfur clusters↗

DOE OSTI2025

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT

NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.

Sun, He↗

DOE OSTI2026

Atomic‐Scale Surface Imaging of Bulk Epitaxial CsPbBr 3 Perovskite Single Crystals on Mica Using Light Assisted Scanning Tunneling Microscopy at Low‐Temperature (80 K)

Epitaxial single-crystalline CsPbBr 3 perovskite films on mica, prepared ex situ, are explored using a low-temperature scanning tunneling microscope (STM) by probing the unoccupied electronic states of their surface in ultra-high vacuum (UHV) at 80 K. Light-assisted STM measurements under a broadband illumination with visible light were employed to enhance and stabilize surface conductivity. STM imaging across the surface of macroscopic bulk CsPbBr 3 films reveals large flat terraces characterized by a specific type of surface reconstruction, consisting of parallel rows of U-shaped atomic nanostructures. These structures are spaced by 12 Å and exhibit an internal feature size of 5.1 Å. Density functional theory (DFT) calculations reproduce the experimental observations and reveal a competition between different orthorhombic CsPbBr 3 (110) surface reconstructions: a Cs-rich structure, identified as the most energetically stable, and three alternative Pb–Br-rich reconstructions, which are slightly higher in energy yet remain consistent with the STM data. Additional analyses that explicitly account for the mica substrate exclude the cubic CsPbBr 3 phase and other orthorhombic surface orientations, while showing that variations in the mica surface termination do not alter the preferred CsPbBr 3 (110) reconstruction. In conclusion, this combined approach thereby confirms our assignment and resolves previous STM interpretations of CsPbBr 3 .

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗

DOE OSTI2026

Chemical bonding, phase stability and magnetic property in Sm 2 Fe 17 X 3 (X=H, C, N): A first-principles perspective

As a promising alternative to Nd–Fe–B magnets, the critical rare earth free Sm 2 Fe 17 X 3 (X = C, N) exhibits potential for high-performance magnets. However, their poor phase stability remains a major obstacle to developing bulk magnets. We investigated the phase stability and intrinsic magnetic properties of Sm 2 Fe 17 X 3 (X = H, C, N) using first-principles calculations and chemical bond analysis. The formation energies are negative, while the decomposition energies are −1.53, 0.348, and −0.74 eV per formula unit for X = H, C, and N, respectively, which is responsible for the weak thermal stability. Our chemical bond analysis reveals that the bonding asymmetry between Sm–X and Fe–X interactions creates local structural distortions and degrades the phase stability of Sm 2 Fe 17 X 3 . The project Crystal Orbital Hamilton Population (-pCOHP) analysis indicates that the Sm–X bonding remains positive up to the Fermi level, indicating stable bonding interactions. Here, in contrast, the Fe–X bonding becomes negative near the Fermi level, signifying anti-bonding contributions that reduce structural stability. Interstitial atoms X expand the lattice and enhance Fe magnetic moments, but Fe–X bonding suppresses neighboring Fe moments. Electron transfer from Sm to X modifies the valence state of Sm and the crystal field at the site, contributing to enhanced magnetocrystalline anisotropy in Sm 2 Fe 17 X 3 . Among the interstitial elements, carbon and nitrogen—with their larger atomic radius and higher electronegativity—induce greater lattice expansion and form stronger bonds with neighboring Sm and Fe atoms compared to hydrogen. Consequently, Sm 2 Fe 17 X 3 (X = C and N) exhibits better phase stability and significant improvement in magnetic properties.

Chemical bonding↗

DOE OSTI2025

Siegert-pseudostate formulation with B-splines

Siegert states (SSs) serve as a useful basis for studying quantum scattering from finite-range potentials. Since they form a discrete instead of continuous set of eigen-solutions, SSs are convenient for performing electronic structure calculations in atoms, molecules, and plasmas. Numerical instabilities may arise, however, in the computation of SSs if the potential vanishes for some extended region, a situation commonly occurring in plasma calculations. Here, in this paper, we identify the cause of these instabilities as the use of non-localized radial basis functions. We thus advocate the use of localized radial basis functions, in particular B-splines, for more robust computations of SSs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

Here, we present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

CoRE MOF database↗

DOE OSTI2024

Quantum Molecular Charge-Transfer Model for Multistep Auger–Meitner Decay Cascade Dynamics

The fragmentation of molecular cations following inner-shell decay processes in molecules containing heavy elements underpins the X-ray damage effects observed in X-ray scattering measurements of biological and chemical materials, as well as in medical applications involving Auger electron-emitting radionuclides. Traditionally, these processes are modeled using simulations that describe the electronic structure at an atomic level, thereby omitting molecular bonding effects. This work addresses the gap by introducing a novel approach that couples Auger–Meitner decay to nuclear dynamics across multiple decay steps, by developing a decay spawning dynamics algorithm and applying it to potential energy surfaces characterized with ab initio molecular dynamics simulations. We showcase the approach on a model decay cascade following K-shell ionization of IBr and subsequent Kβ fluorescence decay. We examine two competing channels that undergo two decay steps, resulting in ion pairs with a total 3+ charge state. This approach provides a continuous description of the electron transfer dynamics occurring during the multistep decay cascade and molecular fragmentation, revealing the combined inner-shell decay and charge transfer time scale to be approximately 75 fs. In conclusion, our computed kinetic energies of ion fragments show good agreement with experimental data.

Ab initio molecular dynamics↗

DOE OSTI2024

Activation of H 2 O by ThO 2 – Experimental and Computational Studies

Here, a synergetic study that utilized anion photoelectron spectroscopy and high-level abinitio calculations has explored the activation of H 2 O molecules by ThO 2 – molecular anions. Both experiment and theory found conclusive evidence for said activation. In the experiments, this appeared as a tell-tale directional shift in the spectral profile of the anionic complex that ruled out physisorption,i.e., ThO 2 – (H 2 O), and implied chemisorption. In the computations, good agreement was found between the calculated and measured vertical detachment energies, and the atomic connectivity (the structure) of the resulting anionic complex was found to be [OTh(OH) 2 ] – .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Exfoliation of Cu-Containing Poly(triazine imide): From Three-Dimensional to Two-Dimensional Particle Morphology

Controlling the morphological parameters of extended covalent organic frameworks remains challenging and represents an important yet often elusive metric of consideration. Typically, carbon nitride materials possess local ordering but remain largely amorphous in terms of their long-range order and orientation. This study probes the synthesis of a crystalline carbon nitride, poly(triazine imide) lithium bromide which possesses an atomically-precise extended structure, and demonstrates its exfoliation into a two-dimensional hexagonal sheet-like morphology. Furthermore, a previously unreported carbon nitride material, poly(triazine imide) copper bromide, or PTI-CuBr, was developed through an additional flux-assisted cation-exchange process and is shown to retain its internal Cu cations during solvothermal exfoliation. Characterization by dynamic light scattering and high-angle annular dark-field scanning electron microscopy reveals the morphological changes and captures the high aspect ratio of the thin carbon nitride sheets with <10 nm thickness while maintaining hundreds of nm in width. Additional characterization by energy-dispersive spectroscopy and X-ray photoelectron spectroscopy confirms that the Cu:Br:N molar ratio was maintained within the extended layers throughout the exfoliation process. This top-down synthesis approach differs from typical methods that isolate thin sheets for subsequent metal−cation coordination and illustrates the importance of maintaining oxygen-free conditions to minimize copper clustering. Thus, this new approach is demonstrated to provide a consistent and more homogeneous occupancy of the PTI pore spaces throughout the carbon nitride framework.

Exfoliation↗

DOE OSTI2025

The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior

Lipid membranes are central to cellular life. Complementing experiments, computational modeling has been essential in unraveling complex lipid-biomolecule interactions, crucial in both academia and industry. The Martini model, a coarse-grained force field for efficient molecular dynamics simulations, is widely used to study membrane phenomena but has faced limitations, particularly in capturing realistic lipid phase behavior. Here, we present refined Martini 3 lipid models with a mapping scheme that distinguishes lipid tails that differ by just two carbon atoms, enhancing the structural resolution and thermodynamic accuracy of model membrane systems including ternary mixtures. The expanded Martini lipid library includes thousands of models, enabling simulations of complex and biologically relevant systems. These advancements establish Martini as a robust platform for lipid-based simulations across diverse fields.

Lipids↗

DOE OSTI2025

Graphene-driven correlated electronic states in one dimensional defects within WS2

Tomonaga-Luttinger liquid (TLL) behavior in one-dimensional systems has been predicted and shown to occur at semiconductor-to-metal transitions within two-dimensional materials. Reports of one-dimensional defects hosting a Fermi liquid or a TLL have suggested a dependence on the underlying substrate, however, unveiling the physical details of electronic contributions from the substrate require cross-correlative investigation. Here, we study TLL formation within defectively engineered WS2 atop graphene, where band structure and the atomic environment is visualized with nano angle-resolved photoelectron spectroscopy, scanning tunneling microscopy and spectroscopy, and non-contact atomic force microscopy. Correlations between the local density of states and electronic band dispersion elucidated the electron transfer from graphene into a TLL hosted by one-dimensional metal (1DM) defects. It appears that the vertical heterostructure with graphene and the induced charge transfer from graphene into the 1DM is critical for the formation of a TLL.

Rossi, Antonio↗

DOE OSTI2024

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advances in Operando Electrocatalysis with High-Resolution Hard X-Ray Spectroscopy

Electrocatalysis unfolds at the interface—where solids, liquids, and gases meet to exchange charge, transform molecules, and set the foundation for technologies like CO 2 conversion, and water splitting. These interfacial regions are inherently dynamic, chemically diverse, and structurally heterogeneous. How atoms rearrange, oxidize, coordinate ligands, or adsorb reaction intermediates under electro-chemical potential defines a catalyst’s performance. Furthermore, capturing these changes with element specificity and electronic sensitivity under operando conditions remains one of the most critical challenges in the field.

Garcia-Esparza, Angel T. [SLAC National Accelerato↗

DOE OSTI2024

Adsorption of Ag, Au, Cu, and Ni on MoS 2 : theory and experiment

Abstract Here, we present results of a computational and experimental study of adsorption of various metals on MoS2. In particular, we analyzed the binding mechanism of four metallic elements (Ag, Au, Cu, Ni) on MoS2. Among these elements, Ni exhibits the strongest binding and lowest mobility on the surface of MoS2. On the other hand, Au and Ag bond very weakly to the surface and have very high mobilities. Our calculations for Cu show that its bonding and surface mobility are between these two groups. Experimentally, Ni films exhibit a composition characterized by randomly oriented nanoscale clusters. This is consistent with the larger cohesive energy of Ni atoms as compared with their binding energy with MoS2, which is expected to result in 3D clusters. In contrast, Au and Ag tend to form atomically flat plateaued structures on MoS2, which is contrary to their larger cohesive energy as compared to their weak binding with MoS2. Cu displays a surface morphology somewhat similar to Ni, featuring larger nanoscale clusters. However, unlike Ni, in many cases Cu exhibits small plateaued surfaces on these clusters. This suggests that Cu likely has two competing mechanisms that cause it to span the behaviors seen in the Ni and Au/Ag film morphologies. These results indicate that calculations of the initial binding conditions could be useful for predicting film morphologies. In addition, out calculations show that the adsorption of adatoms with odd electron number like Ag, Au, and Cu results in 100% spin-polarization and integer magnetic moment of the system. Adsorption of Ni adatoms, with even electron number, does not induce a magnetic transition.

Harms, Haley↗

DOE OSTI2025

Review: moiré-of-moiré superlattice in twisted trilayer graphene

When a third layer of graphene is transferred on top of twisted bilayer graphene with a second twist, the atomic and electronic structure of the system can be significantly enriched. Generally, the two coexisting moiré superlattices give rise to a higher-order moiré-of-moiré (MoM) superlattice, with a plethora of new length scales and associated novel quantum phenomena. This article reviews the current theoretical understanding and experimental observations of twisted trilayer graphene MoM (or supermoiré) superlattices, and the theoretical predictions and ongoing experimental efforts for unraveling rich exotic quantum phenomena and underlying physics.

electron correlation↗