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At least 19 records

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗

Quantum Emitter Formation Dynamics and Probing of Radiation-Induced Atomic Disorder in Silicon

Near-infrared color centers in silicon are emerging candidates for on-chip integrated quantum emitters, optical-access quantum memories, and sensing. We access ensemble G-color-center formation dynamics and radiation-induced atomic disorder in silicon for a series of megaelectronvolt proton-flux conditions. The photoluminescence results reveal that the G centers are formed more efficiently by pulsed-proton irradiation than by continuous-wave proton irradiation. The enhanced transient excitations and dynamic annealing within nanoseconds allows optimization of the ratio of G-center formation to nonradiative defect accumulation. The G centers preserve narrow line widths of about 0.1 nm when they are generated by moderate pulsed-proton fluences, while the line width broadens significantly as the pulsed-proton fluence increases. This implies vacancy or interstitial clustering by overlapping collision cascades. The tracking of G-center properties for a series of irradiation conditions enables sensitive probing of atomic disorder, serving as a complementary analytical method for sensing damage accumulation. Aided by ab initio electronic structure calculations, we provide insight into the atomic disorder induced inhomogeneous broadening by introducing vacancies, silicon interstitials, and oriented strain fields in the vicinity of a G center. A vacancy leads to a tensile strain and can result in either a red shift or a blue shift of the G-center emission, depending on its position relative to the G center. Meanwhile, Si interstitials lead to compressive strain, which results in a monotonic red shift. In conclusion, high-flux and tunable ion pulses enable the exploration of the fundamental dynamics of radiation-induced defects as well as methods for the optimization of G-center formation and qubit synthesis for quantum information processing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Enhancing superconductivity of Y 5 Rh 6 Sn 18 by atomic disorder

Here, we investigate the effect of enhancement of superconducting transition temperature $T_c$ by nonmagnetic atom disorder in the filled skutterudite-related tetragonal Y 5 Rh 6 Sn 18 compound doped with Ca. We documented experimentally that Y 5 Rh 6 Sn 18 and its Ca-doped alloys are electronically inhomogeneous at the nanoscale, when Ca content is smaller than ~1.5 in the Y 5– x Ca x Rh 6 Sn 18 system, while for $\textit{x}$ ≥ 1.5 much stronger chemical phase inhomogeneity is observed with an about 20% volume fraction of the second 3:4:13 cubic phase, which we interpret as a fluctuation of stoichiometry within the bulk sample. Then the enhancement of $T_c$ vs $\textit{x}$ could be modeled by a mechanism proposed in recent theoretical reports for increasing the mean-field transition temperature $T_c$ in the presence of nonmagnetic disorder. The increase in disorder with doping increases the sample inhomogeneity and causes a systematic increase in $T_c$, while for two-phase $\textit{x}$ ≥ 1.5 samples the critical temperature $T^{\star}_c ≈ 2 × T_c$ rapidly increases. Based on band structure calculations performed under pressure, we demonstrate how the change in density of states would affect $T_c$ of Y 5 Rh 6 Sn 18 . We obtained the Grüneisen parameter $γ_G$ larger for the inhomogeneous phase with respect to $γ_G$ of the bulk $T_c$ phase and attribute the enhancement of $T_c$ to larger stiffening of the high-temperature $T^{\star}_c$ phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Metallicity, Atomic Disorder, and Li-Ion Storage in Fast-Charging Anodes

Oxides of Nb with Wadsley-Roth shear structures comprise a family of stable, high-rate anode materials for Li-ion batteries. A particular pair of them offers the unusual opportunity to test how important metallic conduction of the starting electrode is for electrode performance. The selected pair of compounds with similar 4 × 3 Wadsley-Roth block structures are insulating Ti 2 Nb 10 O 29 and metallic Nb 12 O 29 . A combination of diffraction, electrochemistry, magnetic measurements, and entropic potential measurements is employed to establish key findings for these two anode materials. We find that starting with a metallic oxide is not especially advantageous over a comparable material that readily transitions into a metallic state upon lithiation. Second, the rate performance appears to be dictated by ion mobility, and atomic Ti/Nb disorder in Ti 2 Nb 10 O 29 contributes to improved capacity retention at high rates by suppressing Li-ion ordering. However, subtle details in the nature of redox processes make Nb 12 O 29 a slightly better electrode material for long-term cycling at slower rates.

crystal structure↗

Dataset of simulated vibrational density of states and X-ray diffraction profiles of mechanically deformed and disordered atomic structures in Gold, Iron, Magnesium, and Silicon

This dataset is comprised of a library of atomistic structure files and corresponding X-ray diffraction (XRD) profiles and vibrational density of states (VDoS) profiles for bulk single crystal silicon (Si), gold (Au), magnesium (Mg), and iron (Fe) with and without disorder introduced into the atomic structure and with and without mechanical loading. Included with the atomistic structure files are descriptor files that measure the stress state, phase fractions, and dislocation content of the microstructures. All data was generated via molecular dynamics or molecular statics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. This dataset can inform the understanding of how local or global changes to a materials microstructure can alter their spectroscopic and diffraction behavior across a variety of initial structure types (cubic diamond, face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) for Si, Au, Mg, and Fe, respectively) and overlapping changes to the microstructure (i.e., both disorder insertion and mechanical loading).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Abstract Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

36 MATERIALS SCIENCE↗

Imaging antiferromagnetic domain fluctuations and the effect of atomic scale disorder in a doped spin-orbit Mott insulator

Correlated oxides can exhibit complex magnetic patterns. Understanding how magnetic domains form in the presence of disorder and their robustness to temperature variations has been of particular interest, but atomic scale insight has been limited. We use spin-polarized scanning tunneling microscopy to image the evolution of spin-resolved modulations originating from antiferromagnetic (AF) ordering in a spin-orbit Mott insulator perovskite iridate Sr 3 Ir 2 O 7 as a function of chemical composition and temperature. We find that replacing only several percent of lanthanum for strontium leaves behind nanometer-scale AF puddles clustering away from lanthanum substitutions preferentially located in the middle strontium oxide layer. Thermal erasure and reentry into the low-temperature ground state leads to a spatial reorganization of the AF puddles, which nevertheless maintain scale-invariant fractal geometry in each configuration. Our experiments reveal multiple stable AF configurations at low temperature and shed light onto spatial fluctuations of the AF order around atomic scale disorder in electron-doped Sr 3 Ir 2 O 7 .

36 MATERIALS SCIENCE↗

Influence of temperature, doping, and amorphization on the electronic structure and magnetic damping of iron

Hybrid magnonic quantum systems have drawn increased attention in recent years for coherent quantum information processing, but too large magnetic damping is a persistent concern when metallic magnets are used. Their intrinsic damping is largely determined by electron-magnon scattering induced by spin-orbit interactions. In the low scattering limit, damping is dominated by intra-band electronic transitions, which has been theoretically shown to be proportional to the electronic density of states at the Fermi level. In this work, we focus on body-centered-cubic iron as a paradigmatic ferromagnetic material. We comprehensively study its electronic structure using first-principles density functional theory simulations and account for finite lattice temperature, boron (B) doping, and structure amorphization. Our results indicate that temperature induced atomic disorder and amorphous atomic geometries only have a minor influence. Instead, boron doping noticeably decreases the density of states near the Fermi level with an optimal doping level of 6.25%. In addition, we show that this reduction varies significantly for different atomic geometries and report that the highest reduction correlates with a large magnetization of the material. Furthermore, this may suggest materials growth under external magnetic fields as a route to explore in experiment.

36 MATERIALS SCIENCE↗

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗

Disorder in M n+1 AX n phases at the atomic scale

Atomic disordering in materials alters their physical and chemical properties and can subsequently affect their performance. In complex ceramic materials, it is a challenge to understand the nature of structural disordering, due to the difficulty of direct, atomic-scale experimental observations. Here we report the direct imaging of ion irradiation-induced antisite defects in M n+1 AX n phases using double CS-corrected scanning transmission electron microscopy and provide compelling evidence of order-to-disorder phase transformations, overturning the conventional view that irradiation causes phase decomposition to binary fcc-structured M n+1 AX n . With the formation of uniformly distributed cation antisite defects and the rearrangement of X anions, disordered solid solution γ-(M n+1 A)X n phases are formed at low ion fluences, followed by gradual transitions to solid solution fcc-structured (M n+1 A)X n phases. This study provides a comprehensive understanding of the order-to-disorder transformations in M n+1 AX n phases and proposes a method for the synthesis of new solid solution (M n+1 A)X n phases by tailoring the disorder.

36 MATERIALS SCIENCE↗

Controlling Interlayer Disorder Toward Reversible Phase Transition in a Layered Sodium Manganese Oxide Cathode

Sodium manganese oxides are promising Na-ion battery cathodes but they suffer from irreversible phase transitions during electrochemical reactions. Most strategies to date have aimed to suppress the phase transitions by stabilizing their layered structures through limiting the content of extractable Na + . Here, we conversely increase atomic disorder in the Na-birnessite, a layered sodium manganese oxide, and thereby modulate its phase transition behavior toward improved electrochemical reversibility. Our study reveals that Mn vacancies and Mn migrated into the interlayer affect interlayer local environment of Na + and water molecules consequently enhancing Na + mobility. We observe better capacity retention for disordered “D-Na-birnessite”, which undergoes a reversible phase transition from the birnessite-type structure to an O′3-type α-Na x MnO 2 -like structure through another intermediate metastable birnessite-type phase. In conclusion, this research highlights the positive effects of atomic disorder to regulate phase transition routes for achieving superior electrochemical reversibility, finally paving the way to overcoming the limits of layered oxide cathodes.

Kang, Seongkoo [Korea Univ., Seoul, (Korea, Republ↗

Structural tunability and origin of two-level systems in amorphous silicon

Amorphous silicon films prepared by electron-beam evaporation have systematically and substantially greater atomic density for higher thickness, higher growth temperature, and slower deposition rate, reaching the density of crystalline Si when films of thickness greater than ~300 nm are grown at 425 °C and at <1 Å/s. Here, a combination of spectroscopic techniques provide insight into atomic disorder, local strains, dangling bonds, and nanovoids. Electron diffraction shows that the short-range order of the amorphous silicon is similar at all growth temperatures, but fluctuation electron microscopy shows that films grown above room temperature show a form of medium-range order not previously observed in amorphous silicon. Atomic disorder and local strain obtained from Raman spectroscopy are reduced with increasing growth temperature and show a nonmonotonic dependence on thickness. Dangling bond density decreases with increasing growth temperature and is only mildly dependent on thickness. Positron annihilation Doppler broadening spectroscopy and electron energy loss spectroscopy show that nanovoids, and not density variations within the network, are responsible for reduced atomic density. Specific heat and mechanical loss measurements, which quantify the density of tunneling two-level systems, in combination with the structural data, suggest that two-level systems in amorphous silicon films are associated with nanovoids and their surroundings, which are in essence loosely bonded regions where atoms are less constrained.

36 MATERIALS SCIENCE↗

Vibrational Properties of Pd Nanocubes

The atomic disorder and the vibrational properties of Pd nanocubes have been studied through a combined use of X-ray diffraction and molecular dynamics simulations. The latter show that the trend of the mean square relative displacement as a function of the radius of the coordination shells is characteristic of the nanoparticle shape and can be described by a combined model: A correlated Debye model for the thermal displacement and a parametric expression for the static disorder. This combined model, supplemented by results of line profile analysis of the diffraction patterns collected at different temperatures (100, 200, and 300 K) can explain the observed increase in the Debye–Waller coefficient, and shed light on the effect of the finite domain size and of the atomic disorder on the vibrational properties of metal nanocrystals.

36 MATERIALS SCIENCE↗

Violation of the Cauchy–Born rule in multi-principal element alloys

Multi-principal element alloys are a novel class of materials that are formed by combining multiple elements in high concentrations and show exceptional properties compared to conventional alloys. These alloys have high configurational entropy due to inherent atomic disorder. The Cauchy–Born rule is a popular homogenization method for linking atomistic to continuum length scales. In this Letter, we use ab initio density functional theory calculations to report that the Cauchy–Born rule, which holds in conventional alloys in the absence of defects, is not valid in multi-principal element alloys. Further, the violation of the Cauchy–Born rule in these alloys is attributed to the presence of atomic disorder due to which the deformations are inhomogeneous. Our results also show the deviations in deformation are related to the magnitude of the stretch and shear by power laws.

36 MATERIALS SCIENCE↗

Atomic step disorder on polycrystalline surfaces leads to spatially inhomogeneous work functions

Structural disorder causes materials’ surface electronic properties, e.g., work function (ϕ), to vary spatially, yet it is challenging to prove exact causal relationships to underlying ensemble disorder, e.g., roughness or granularity. For polycrystalline Pt, nanoscale resolution photoemission threshold mapping reveals a spatially varying ϕ=5.70±0.03 eV over a distribution of (111) vicinal grain surfaces prepared by sputter deposition and annealing. With regard to field emission and related phenomena, e.g., vacuum arc initiation, a salient feature of the ϕ distribution is that it is skewed with a long tail to values down to 5.4 eV, i.e., far below the mean, which is exponentially impactful to field emission via the Fowler–Nordheim relation. We show that the ϕ spatial variation and distribution can be explained by ensemble variations of granular tilts and surface slopes via a Smoluchowski smoothing model wherein local ϕ variations result from spatially varying densities of electric dipole moments, intrinsic to atomic steps, that locally modify ϕ. Atomic step-terrace structure is confirmed with scanning tunneling microscopy (STM) at several locations on our surfaces, and prior works showed STM evidence for atomic step dipoles at various metal surfaces. From our model, we find an atomic step edge dipole μ=0.12 D/edge atom, which is comparable to values reported in studies that utilized other methods and materials. Our results elucidate a connection between macroscopic ϕ and the nanostructure that may contribute to the spread of reported ϕ for Pt and other surfaces and may be useful toward more complete descriptions of polycrystalline metals in the models of field emission and other related vacuum electronics phenomena, e.g., arc initiation.

36 MATERIALS SCIENCE↗