Atomic Structure Research Articles

A brief review on Ce and Zr-based phase-separated metallic glasses

AbstractPhase-separated metallic glasses (MGs) have attracted a lot of interest recently because they offer a unique opportunity to design composites or alloys with hierarchical microstructure at various length scales. Phase-separated MGs differ from other MGs in terms of their structure and physical properties. Though a lot of theoretical work has been done, there is still a lack of understanding regarding the mechanism underlying phase separation in MGs. In general, phase separation in many MG systems is explained on the basis of nucleation and growth or spinodal decomposition mechanisms. On the other hand, the phase separation in Ce-based MGs is examined based on changes in the electronic structure of Ce atoms. This opens up a new direction of research for delineating issues pertaining to phase separation in amorphous systems. The present brief review aims to provide a comprehensive overview of the phase separation phenomenon in Ce- and Zr-based MG systems. It is broadly divided into two sections: the first section gives a brief introduction into the phase separation in MG systems, mechanisms of phase separation, micro-structural and thermal characteristics, and advantages of phase separation. The second section discusses some of the recent work on Ce- and Zr-based phase-separated MGs with respect to their design and properties. Graphical Abstract

Optimized Second-Generation IL-4Rα Inhibition: Structural and Molecular Dynamics Properties of Rademikibart Fab-IL-4Rα Complex

Introduction: Global Burden of Disease studies showed atopic dermatitis (AD) and asthma are the top two immune-mediated inflammatory diseases at younger age [1]. Dupilumab was the initial first-generation interleukin-4 receptor alpha (IL-4Rα) inhibitor for treating both type I and type II IL-4Rα-dependent inflammatory disorders. Following recent clinical trials, rademikibart (previously, CBP-201) emerges as an optimized next-generation human monoclonal antibody with higher binding affinity to IL-4Rα compared to dupilumab [2]. It demonstrated better effect in inhibiting STAT6 intracellular signaling in vitro and provided similar potency inhibiting both IL-4 induced TARC release and IL-4 induced B cell activation [2]. Materials & Methods: X-ray crystallography was used to determine the atomic resolution 3D structure of rademikibart fragment antigen binding (Fab) bound to IL-4Rα. This structure was analyzed and compared computationally with the 2.82 Å resolution crystal structure of dupilumab Fab bound to IL-4Rα (Protein Data Bank Code 6WGL). Molecular dynamics studies on rademikibart and dupilumab bound to IL-4Rα examined the stability of the complexes and effects of amino acid mutations on complex formation. Results: The x-ray crystal structure of rademikibart Fab bound to IL-4Rα was determined at 2.71Å and compared to the complex of dupilumab Fab and IL-4Rα. The rotation angle between dupilumab and rademikibart bound to IL- 4Rα is 59.17°. This rotation enables the epitope of rademikibart, but not dupilumab, on IL-4Rα to overlap more closely with the conserved binding interface utilized by IL-4 and IL-13 cytokines. Molecular dynamics simulations of rademikibart Fab and dupilumab Fab complexed with IL-4Rα showed the third interface loop (residues 148 to 152 in domain 2) of IL-4Rα interacts directly with rademikibart, which is absent in dupilumab/IL- 4Rα complex. This finding is confirmed by analysis of the hydrogen bond interactions at the interface between the antibodies and IL-4Rα, demonstrating superior binding energy for rademikibart. Through single amino acid mutation analysis on rademikibart, we identified residue Y50 on rademikibart as the key residue interacting with IL- 4Rα’s third interface loop. Conclusion: Our data provide a molecular and structural rationale for the enhanced IL-4Rα inhibition by rademikibart over dupilumab, confirming rademikibart as an optimized second-generation IL-4Rα inhibitor.

Revealing the correlation between asymmetric structure and low thermal conductivity in Janus-graphene via machine learning force constant potential

Understanding the fundamental link between structure and functionalization is crucial for designing and optimizing functional materials, since different structural configurations could trigger materials to demonstrate diverse physical and chemical properties. However, the correlation between crystal structure and thermal conductivity (κ) remains unclear. In this study, taking two-dimensional (2D) carbon allotropes Janus-graphene and graphene as study cases, we utilize phonon Boltzmann transport equation combined with machine learning potential to thoroughly investigate the complex folding structure of pure sp2 hybridized Janus-graphene from the perspective of crystal structure, phonon modal resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and κ. The results reveal that the folded structure in Janus-graphene causes strong symmetry breaking, significantly reduces phonon group velocities, increases phonon–phonon scattering, and ultimately leads to low κ. These findings enhance our understanding of how atomic structure folding affects thermal transport and the relationship between structure and functionalization.

High-Throughput UV-Induced Synthesis and Screening of Alloy Electrocatalysts.

The combination of different elements in alloy catalysts can lead to improved activity as it provides opportunities to tune the electronic structures of surface atoms. However, the synthesis and performance screening of alloy catalysts through a vast chemical space are cost- and labor-intensive. Herein, a UV-induced, high-throughput method is reported for the synthesis and screening of alloy electrocatalysts in a fast and low-cost manner. A platform that integrates 37 mini-reaction-cells enables simultaneous UV-induced photodeposition of alloy nanoparticles with up to 37 compositions. These mini-reaction-cells further allow a transfer-free, high-throughput electrochemical performance screening. Binary (PtPd, PtIr, PdIr), ternary (PtPdIr, PtRuIr) and quaternary (PtPdRuIr) alloys have been synthesized with the activity of the binary alloys (57 compositions) for hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) being screened. The predicted high performance of identified alloy compositions are subsequently validated by standard measurements using a rotating disk electrode configuration. It is found that the as-synthesized alloy nanoparticles are rich in twin boundaries and thus possess lattice strain. Density functional theory calculation implies that the high ORR activity of the screened Pt0.75Pd0.25 alloy originates from the interplay between the differentiated adsorption sites because of alloying and the strain-induced modulation of the d-band center.

Heterostructural interface engineering for ultrawide-gap nitrides from first principles: TaC / AlN and TaC / GaN rocksalt-wurtzite interfaces

Epitaxial lattice matching is an important condition for the formation of coherent interfaces with low defect densities. However, lattice-matched substrates with the same crystal structure as the active layer are often not available, suggesting opportunities for utilizing heterostructural interfaces. For example, at high Al contents that are interesting for ultrawide-gap applications in power electronics, AlxGa1−xN semiconductor alloys in the (0001) orientation of the wurtzite (wz) structure become lattice-matched to (111)-oriented rocksalt (rs) TaC substrates. To predict the expected interface atomic structures under different synthesis conditions, we perform high-throughput density-functional-theory calculations, using an algorithm for systematic sampling of the possible stacking sequences of the atomic layers on the in-plane hexagonal lattice. The approach considers octahedral, tetrahedral, and prismatic coordination motifs, and is generally applicable for the modeling of commensurate rs/wz heterostructural interfaces. Our results provide guidance for synthesis control of substrate-film bonding and the polarity of ultrawide-gap AlxGa1−xN alloys on TaC substrates. Published by the American Physical Society 2024

Theoretical analysis and predictions for the two-neutrino double electron capture of 124Xe

Abstract We provide a complete theoretical description of the two-neutrino electron capture in 124Xe improving both the nuclear and the atomic structure calculations. We improve the general formalism through the use of the Taylor expansion method, leading to higher order terms in the decay rate of the process. The nuclear part is treated with pn-QRPA and interacting shell model (ISM) methods. The nuclear matrix elements (NMEs) are calculated with the pn-QRPA method with isospin restoration by fixing the input parameters so that the experimental decay rate is reproduced, resulting in values significantly lower than in previous calculations. The validity of the pn-QRPA NMEs is tested by showing their values to be comparable with the ones for double-beta decay with emission of two electrons of 128,130Te, which have similar pairing features. Within the ISM, we reproduce the total experimental half-life within a factor of two and predict the capture fraction to the KK channel of about 74%. We also predict the capture fractions to other decay channels and show that for the cumulative decay to the KL1-KO1 channels, a capture fraction of about 24% could be observed experimentally. On the atomic side, calculations are improved by accounting for the Pauli blocking of the decay of innermost nucleon states and by considering all s-wave electrons available for capture, expanding beyond the K and L1 orbitals considered in previous studies. We also provide improved atomic relaxation energies of the final atomic states of 124Te, which may be used as input for background modeling in liquid Xenon experiments.

Tailoring Superatomic Stability with Transition Metals in Silicon Cages: Shrinking to M@Si15 (M = Re, Os, Ir).

The design of materials with intriguing electronic properties is crucial for advancing nanoscale technologies, where precise control over atomic structure and electronic behavior is essential. Metal-encapsulating silicon cage superatoms (SAs) provide a new paradigm for molecular-scale material design, allowing fine-tuning of both structure and electronic characteristics. The formation of superatoms mimicking halogens, noble gases, and alkali metals has been well-studied, particularly with M@Si16, where early transition metals from groups 3 to 5 stabilize within a Si16 cage, achieving a 68-electron configuration. For late transition metals with excess electrons, a Si15 cage offers enhanced stability by fulfilling the 68-electron rule with one fewer Si atom. This research synthesizes Si15 cage-SAs with rhenium (Re) from group 7 and iridium (Ir) from group 9 on p-type and n-type organic substrates. The stability of Re@Si15 and Ir@Si15 is evaluated via oxidative reactivity with X-ray photoelectron spectroscopy and theoretical calculations, including osmium (Os) from group 8. Re@Si15-, Os@Si150, and Ir@Si15+ exhibit superatomic behaviors similar to halogens, noble gases, and alkali metals due to the 68-electron shell closure. Among them, Re@Si15- on p-type organic substrates shows superior electronic and geometric properties. These findings advance our understanding of M@Sin systems for transition metals, addressing longstanding questions about their properties at n = 15 and 16.

Exploring Structural Anisotropy in Amorphous Tb-Co via Changes in Medium-Range Ordering.

Amorphous thin films grown by magnetron co-sputtering exhibit changes in atomic structure with varying growth and annealing temperatures. Structural variations influence the bulk properties of the films. Scanning nanodiffraction performed in a transmission electron microscope (TEM) is applied to amorphous Tb17Co83 (a-Tb-Co) films deposited over a range of temperatures to measure relative changes in medium-range ordering (MRO). These measurements reveal an increase in MRO with higher growth temperatures and a decrease in MRO with higher annealing temperatures. The trend in MRO indicates a relationship between the growth conditions and local atomic ordering. By tilting select films, the TEM measures variations in the local atomic structure as a function of orientation within the films. The findings support claims that preferential ordering along the growth direction results from temperature-mediated adatom configurations during deposition, and that oriented MRO correlates with increased structural anisotropy, explaining the strong growth-induced perpendicular magnetic anisotropy found in rare earth-transition metal films. Beyond magnetic films, we propose the tilted FEM workflow as a method of extracting anisotropic structural information in a variety of amorphous materials with directionally dependent bulk properties, such as films with inherent bonding asymmetry grown by physical vapor deposition.

Secondary Structure Detection and Structure Modeling for Cryo-EM.

Rapid advancements in cryogenic electron microscopy (cryo-EM) have revolutionized the field of structural biology by enabling the determination of complex macromolecular structures at unprecedented resolutions. When cryo-EM density maps have a resolution around 3Å, the atomic structure can be modeled manually. However, as the resolution decreases, analyzing these density maps becomes increasingly challenging. For modeling structures in lower resolution maps, deep learning can be used to identify structural features in the maps to assist in structure modeling.Here, we present a suite of deep learning-based tools developed by our lab that enable structural biologists to work with cryo-EM maps of a wide range of resolutions. For cryo-EM maps at near-atomic resolution (5Å or better), DeepMainmast automatically models all-atom structures by tracing the main chain from local map features of amino acids and atoms detected by deep learning; DAQ score quantifies map-model fit and indicates potential misassignments in protein models. In intermediate resolution maps (5-10Å), Emap2sec and Emap2sec+ can accurately detect protein secondary structures and nucleic acids. These tools and more are available at our web server: https://em.kiharalab.org/ .

Optimization of Laser Based-Powder Bed Fusion Parameters for Controlled Porosity in Titanium Alloy Components

Laser based-powder bed fusion (LB-PBF) enables fast, efficient, and cost-effective production of high-performing products. While advanced functionalities are often derived from geometric complexity, the capability to tailor material properties also offers significant opportunities for technical innovation across many fields. This study explores the optimization of the LB-PBF process parameters for producing Ti6Al4V titanium alloy parts with controlled porosity. To this end, cuboid and lamellar samples were fabricated by systematically varying laser power, hatch distance, and layer thickness according to a full factorial Design of Experiments, and the resulting specimens were thoroughly characterized by analyzing envelope porosity, surface roughness and waviness, surface morphology, and surface area. A selection of specimens was further examined using small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) to investigate the atomic structure and nanometric porosity of the material. The results demonstrated the possibility to finely control the porosity and surface characteristics of Ti6Al4V within specific LB-PBF process ranges. The pores were found to be mostly closed even for thin walls, while the surface roughness was recognized as the primary factor impacting the surface area. The lamellar samples obtained by exposing single scan tracks showed nearly an order-of-magnitude increase in both surface area and pore volume, thereby laying the groundwork for the production of parts with optimized porosity.

Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe

AbstractTo advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic level control of general grain boundaries (GGBs), which govern the material properties, is achieved. However, owing to the complex and diverse structures of GGBs, there have been no reports on interatomic potentials capable of reproducing them. This accuracy is essential for conducting molecular dynamics analyses to derive material design guidelines. In this study, we constructed a machine learning interatomic potential (MLIP) with density functional theory (DFT) accuracy to model the energy, atomic structure, and dynamics of arbitrary grain boundaries (GBs), including GGBs, in α-Fe. Specifically, we employed a training dataset comprising diverse atomic structures generated based on crystal space groups. The GGB accuracy was evaluated by directly comparing with DFT calculations performed on cells cut near GBs from nano-polycrystals, and extrapolation grades of the local atomic environment based on active learning methods for the entire nano-polycrystal. Furthermore, we analyzed the GB energy and atomic structure in α-Fe polycrystals through large-scale molecular dynamics analysis using the constructed MLIP. The average GB energy of α-Fe polycrystals calculated by the constructed MLIP is 1.57 J/m2, exhibiting good agreement with experimental predictions. Our findings demonstrate the methodology for constructing an MLIP capable of representing GGBs with high accuracy, thereby paving the way for materials design based on computational materials science for polycrystalline materials.

In Silico Efficacy of Oleic Acid, n-Hexanoic acid, and Epiglobulol Obtained from Conocarpus Lancifolius against Class D β-lactamases

Introduction: Antimicrobial resistance poses an escalating global challenge, compounded by the sluggish pace of new antibiotic discovery. Objectives: This study undertook an in silico analysis of phytochemicals derived from Conocarpus lancifolius, a local plant in the Jazan region of Saudi Arabia, to investigate potential antibacterial agents. Methods: The 3D crystallographic structure of OXA-48 was sourced from the RCSB protein databank (PDBID: 3HBR) and pre-processed. The Lib Dock algorithm facilitated the analysis of optimal binding orientations and potential interactions between compounds and the 3HBR. Results: The docking analysis outcome revealed Oleic acid as the most active compound and the binding mode exhibited that the -OH group of the terminal carboxylic acid moiety formed hydrogen bonds with Thr197 and Ala207, and the oxygen atom showed bonding with Lys116. Moreover, N-hexanoic acid also demonstrated promising binding interactions with the 3HBR, displaying hydrogen bond interactions with Lys208, Tyr211, and Ser70, along with hydrophobic alkyl interactions with Lys208 and Met115 at the protein's active sites. Unfavourable donor-donor and acceptor-acceptor bindings were also observed with Ser70 and Arg250 residues. Conclusions: The OXA-48 and NDM-1 predominate in countries of the Arabian Peninsula. Our findings suggest oleic acid as a promising candidate for combating OXA-48 β-lactamase, pending further assessment of its atomic structure via X-ray crystallography.

L10-PtCo and L12-Pt3Co Intermetallics for Oxygen Reduction Reaction: The Influence of Composition and Structure on Properties.

Pt-based intermetallics are regarded as highly efficient electrocatalysts for oxygen reduction reaction (ORR). However, Pt-based intermetallics with different Pt: M atomic ratios have different atomic arrangements and crystal structures, which will change the electronic structure and coordination environment of Pt, thus affecting the electrocatalytic activity. In this work, we prepared L12-Pt3Co and L10-PtCo intermetallic catalysts by modulating the molar ratio of Pt and Co precursors using a thermal annealing method. The mass activity (MA) of L10-PtCo is 0.52 A mg-1 Pt at 0.9 V, which is 1.44 times larger than that of L12-Pt3Co (0.36 A mg-1 Pt). In addition, the MA of L10-PtCo decreases by 17.31 % after 10,000 CV cycles, which is smaller than that of L12-Pt3Co (25.00 % loss in MA), showing excellent structural stability. Theoretical calculations reveal that compared to L12-Pt3Co, L10-PtCo has more electrons transferred to the Pt sites, which further optimizes the electronic structure of Pt and reduces the d-band center, leading to the increase of the electrocatalytic performance. This work provides new insights into the study of Pt-based intermetallics with different Pt: M ratios, which is helpful for the screening and preparation of high-performance Pt-based intermetallics.

Engineering Topological and Chemical Disorder in Pd Sites for Record-Breaking Formic Acid Electrocatalytic Oxidation.

Designing palladium-based formic acid oxidation reaction (FAOR) catalysts to achieve significant breakthroughs in catalytic activity, pathway selectivity, and toxicity resistance is both urgent and challenging. Here, these challenges are addressed by pioneering a novel catalyst design that incorporates both topological and chemical disorder, developing a new class of PdCuLaYMnW high-entropy amorphous alloys with a porous network (Net-Pd-HEAA) as a highly active, selective, and stable FAOR electrocatalyst. This novel Net-Pd-HEAA demonstrates record-breaking FAOR performance, achieving the mass and specific activities of 5.94 A mgPd -1 and 8.94mA cm-2, respectively, surpassing all previously reported Pd-based catalysts and showing strong competitiveness against advanced Pt-based catalysts. Simulataneously, Net-Pd-HEAA exhibits extraordinary stability in accelerated durability tests (ADT) and chronoamperometry (CA) tests. Advanced characterization and in situ, spectral analysis reveal that the extremely disordered atomic structure effectively regulates the geometric and electronic structure of the Pd sites, enhancing active intermediate coverage, facilitating dehydrogenation pathway, and inhibiting the production/adsorption of CO. Furthermore, when employed as the anode catalyst in proton exchange membrane water electrolysis (PEMWE), Net-Pd-HEAA only requires a potential of 1.28V to obtain a current density of 1 A cm-2, and operates stably in a highly corrosive electrolyte for over 100 h.

Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation.

BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures. Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.

Soft Colloidal Electrode Enabled by Water Distribution Control for Ultra-Stable Aqueous Zn-I Batteries.

Designing effective electrode material is crucial for developing ultra-long lifetime batteries, thereby reducing daily battery costs. Current electrode materials are typically solid or liquid state, with an intermediate colloidal state offering the advantages of fixed redox-active species, akin to solid-state materials, and the absence of rigid atomic structure, akin to liquid-state materials, while avoiding the particle pulverization and uncontrolled migration. Herein, an aqueous Zn||Pluronic F127 (PF127)/ZnI2 colloid battery is developed utilizing the inherent water molecular control effect of ZnSO4. In this system, ZnSO4 in the electrolyte acts as a water molecular valve, regulating the water content within the PF127 polymer to form a PF127 colloid. The resulting aqueous Zn||PF127/ZnI2 colloid battery exhibits an ultra-long cycling lifetime and compatibility with various simulated and practical operating conditions, highlighting its potential for practical applications. Additionally, this battery design concept offers a platform for constructing ultra-stable aqueous batteries.

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Abstract Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

Atomic‐Scale Order and Disorder Induced Diverse Topological Spin Textures in Self‐Intercalated Van der Waals Magnets Cr1+δTe2

AbstractThe intercalation of magnetic atoms into van der Waals (vdW) gaps offers a versatile approach for manipulating local magnetic ordering in vdW magnets. However, these intercalated magnetic atoms are often intricately positioned within the gaps, resulting in an ambiguous impact on local magnetic interactions and spin configurations. Herein, the atomic and magnetic structures of self‐intercalated prototype materials Cr1+δTe2 are comprehensively investigated using transmission electron microscopy, elucidating the correlation between the concentration and distribution of intercalated Cr atoms and the evolution of spin textures. The observed transitions from topological Néel‐type skyrmions to non‐topological type‐II bubbles, and ultimately to trivial ferromagnetic domains, underscore the complex nature of these magnetic spin textures. The complexity of the local structural arrangements is further revealed through integrating atomic‐resolution imaging, revealing the disorder‐order‐disorder transitions manifested by the distribution of intercalated atoms. The modulation of intercalated atomic structure shapes the observed spin textures by affecting the Dzyaloshinskii−Moriya interaction (DMI), inter/intra‐layer coupling, and magnetic anisotropy. These findings provide profound insights into the structure‐magnetism relationship, offering promising avenues for engineering the magnetic properties of vdW magnets at the atomic level.

Molecular Dynamics Simulation of High-Energy Beam Irradiation of SiO2 Single Crystal with Three Different Morphologies: No Free Surface, with a Free Surface, and Thin Films

Abstract High-energy beam irradiation of SiO2 crystal with the α-quartz structure was simulated by the molecular dynamics method. Three types of specimen structures were examined: a single crystal without a free surface, a single crystal with a free surface, and a thin film. After structural relaxation at room temperature, a cylindrical region with diameter of 3.0 nm was set at the center of the specimen and high-thermal energy of Seff = 0.1 – 2.5 keV/nm was added to that region, where Seff is the effective stopping power. Atomic motions were calculated by the molecular dynamics method using the large-scale atomic/molecular massively parallel simulator). Vashishta potential was used for the atomic interaction. The single crystal structure of α-quartz without the free surface was stable up to 1.0 keV/nm and it gradually became amorphous with increasing thermal energy. In contrast, the single crystal structure with the free surface was stable up to 0.5 keV/nm and was amorphous at higher thermal energy. In particular, the atomic structures for the thermal impact Seff ≥ 2.0 keV/nm had a facet-like structure at the impacted surface, which corresponds to actual experimental results. Nano-hole formation was observed in the irradiation process of the film structure.

Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom

Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for machine learning-assisted atomic structure calculations based on the Cowan code’s Hartree–Fock with relativistic corrections (HFR) theory. The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. This semi-empirical framework is applied to calculate and analyze the excited state energy levels of the 4f closed-shell Yb I atom, providing insights into the applicability of different algorithms under various conditions. The reliability and advantages of this innovative approach are demonstrated through comprehensive comparisons with ab initio calculations, experimental data, and other theoretical results.