arXiv subjects

Stephen R. Elliott

Publications and source records attributed to Stephen R. Elliott.

At least 19 recordsLinked to original sources

arXiv2026

A Defect-Free Model of Amorphous Silicon with Pristine Electronic Structure

Amorphous silicon (a-Si) is understood to be the canonical continuous random network material, ideally defined by fully fourfold coordination. Here, we show that a defect-free ('ideal') model of a-Si from machine-learning-driven molecular-dynamics simulations [L. A. M. Rosset et al., Nat. Commun. 16, 2360 (2025)], subsequently evaluated with hybrid-level density-functional theory computations, can accurately reproduce the experimentally observed electronic bandgap. We compare this model with one resulting from the Wooten-Winer-Weaire (WWW) bond-switching approach and with other recent approximants to ideal a-Si. More broadly, our work provides a platform for studies of band tails, optical properties, and transport in a-Si.

cond-mat.mtrl-sci↗

arXiv2025

Medium-range structural order in amorphous arsenic

Medium-range order (MRO) is a key structural feature of amorphous materials, but its origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (a-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structure factor of a-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of a-As with that of its lighter homologue, red amorphous phosphorus (a-P), identifying the dihedral-angle distribution as a key factor differentiating the MRO in both. The pressure-dependent structural behaviors of a-As and a-P differ as well, which we link to the interplay of ring topology and structural entropy. We finally show that the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations.

cond-mat.mtrl-sci↗

arXiv2025

Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential

Computer simulations have long been key to understanding and designing phase-change materials (PCMs) for memory technologies. Machine learning is now increasingly being used to accelerate the modelling of PCMs, and yet it remains challenging to simultaneously reach the length and time scales required to simulate the operation of real-world PCM devices. Here, we show how ultra-fast machine-learned interatomic potentials, based on the atomic cluster expansion (ACE) framework, enable simulations of PCMs reflecting applications in devices with excellent scalability on high-performance computing platforms. We report full-cycle simulations -- including the time-consuming crystallisation process (from digital "zeroes" to "ones") -- thus representing the entire programming cycle for cross-point memory devices. We also showcase a simulation of full-cycle operations, relevant to neuromorphic computing, in a mushroom-type device geometry. Our work provides a springboard for the atomistic modelling of PCM-based memory and neuromorphic computing devices -- and, more widely, it illustrates the power of highly efficient ACE ML models for materials science and engineering.

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arXiv2024

The pathway to chirality in elemental tellurium

Chiral crystals, like chiral molecules, cannot be superimposed onto their mirror images -- a fundamental property that has been linked to interesting physical behavior and exploited in functional devices. Among the simplest inorganic systems with crystallographic chirality, elemental tellurium adopts crystal structures with right- or left-handed chains. However, understanding the formation mechanisms of those structures has been difficult due to the rapid crystallization of Te, which reaches the spatial and temporal resolution limits of even the most advanced experiments. Here, we report ultra-large-scale, quantum-mechanically accurate simulations that reveal mechanisms of crystallization and the origin of crystallographic chirality in solid Te. We identify a characteristic, disordered cube-like structural motif -- a transient bonding environment with only nanosecond lifetime -- that enables both the rapid crystallization of Te and mediates chirality transfer. Based on the resulting microscopic understanding, we are able to explain the switching behavior of Te-based electrical devices.

cond-mat.mtrl-sci↗

arXiv2024

New insights into the origin of the first sharp diffraction peak in amorphous silica from an analysis of chemical and radial ordering

The structural origin of the first sharp diffraction peak (FSDP) in amorphous silica is studied by analyzing chemical and radial ordering of silicon (Si) and oxygen (O) atoms in binary amorphous networks. The study shows that the chemical order involving Si--O and O--O pairs play a major role in the formation of the FSDP in amorphous silica. This is supplemented by small contributions arising from the relatively weak Si--Si correlations in the Fourier space. A shell-by-shell analysis of the radial correlations between Si--Si, Si--O and O--O atoms in the network reveals that the position and the intensity of the FSDP are largely determined by atomic pair correlations originating from the first two/three radial shells on a length scale of about 5--8 Å, whereas the fine structure of the intensity curve in the vicinity of the FSDP is perturbatively modified by atomic correlations arising from the radial shells beyond 8 Å. The study leads to a simple mathematical relationship between the position of the radial peaks ($r_k$) in the partial pair-correlation functions and the diffraction peaks ($Q_k$) that can be used to obtain approximate positions of the FSDP and the principal peak. The results are complemented by numerical calculations and an accurate semi-analytical expression for the diffraction intensity obtained from the partial pair-correlation functions of amorphous silica for a given radial shell.

cond-mat.dis-nn↗

arXiv2023

Understanding defects in amorphous silicon with million-atom simulations and machine learning

The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we show that atomistic machine-learning methods can reveal the complex structural and energetic landscape of defects in amorphous silicon. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and more than ten thousand defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned local atomic energies to develop a universal classification of the different types of defects in amorphous silicon. The results suggest a revision of the established floating-bond model by showing that fivefold-coordinated atoms in amorphous silicon exhibit a wide range of local environments, and it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for modelling and understanding defects in disordered materials beyond silicon alone.

cond-mat.mtrl-sci↗

arXiv2023

Ab initio studies of the impact of the Debye-Waller factor on the structural and dynamical properties of amorphous semiconductors: The case of $a$-Si

This paper presents a first-principles study of the Debye-Waller factor and the Debye temperature for amorphous silicon ($a$-Si) from lattice-dynamical calculations and direct molecular-dynamics simulations using density-functional theory (DFT). The effects of temperature and structural disorder on the intensity of the diffraction maxima and the vibrational mean-square displacement (MSD) of Si atoms are studied in the harmonic approximation, with particular emphasis on the bond-length disorder, the presence of coordination defects, and microvoids in $a$-Si networks. It has been observed that the MSDs associated with tetrahedrally-bonded Si atoms are considerably lower than their dangling-bond counterparts -- originating from isolated and vacancy-induced clustered defects -- and those on the surface of microvoids, leading to an asymmetric non-gaussian tail in the distribution of atomic displacements. An examination of the effect of anharmonicity on the MSD at high temperatures using direct $ab$ $initio$ molecular-dynamics simulations (without the harmonic approximation) suggests that the vibrational motion in $a$-Si is practically unaffected by anharmonic effects at temperatures below 400 K, as far as the present DFT calculations are concerned. The Debye temperature of $a$-Si is found to be in the range of 488--541 K from specific-heat and MSD calculations using first-principles lattice-dynamical calculations in the harmonic approximation, which matches closely with the experimental value of 487--528 K obtained from specific-heat measurements of $a$-Si at low temperatures.

cond-mat.mtrl-sci↗

arXiv2022

Structure and Bonding in Amorphous Red Phosphorus

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bond-ing in a-P from first principles, combining machine-learning and density-functional theo-ry (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phos-phorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations - for example, of a-P-based battery materials.

cond-mat.mtrl-sci↗

arXiv2022

Extended-range order in tetrahedral amorphous semiconductors: The case of amorphous silicon

This paper reports the presence of extended-range ordering in the atomic pair-correlation function of amorphous silicon ($a$-Si) using ultra-large atomistic models obtained from Monte Carlo and molecular-dynamics simulations. The extended-range order manifests itself in the form of radial oscillations, on the length scale of 20-40 angstrom, which are examined by directly analyzing the radial distribution of atoms in distant coordination shells and comparing the same with those from a class of partially-ordered networks of Si atoms and disordered configurations of crystalline silicon from an information-theoretic point of view. The study suggests that the extended-range radial oscillations principally originate from the propagation of radial ordering from the first few atomic shells to a distance of up to 40 angstrom. The effect of these oscillations on the first sharp diffraction peak (FSDP) in the structure factor is addressed by obtaining a semi-analytical expression for the static structure factor of $a$-Si, and calculating an estimate of the error of the intensity of the FSDP associated with the truncation of radial information from distant shells. The results indicate that the extended-range oscillations do not have any noticeable effects on the position and intensity of the FSDP, which are primarily determined by the medium-range atomic correlations of up to a length of 20 angstrom in amorphous silicon.

cond-mat.dis-nn↗

arXiv2019

Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations

Structurally disordered materials continue to pose fundamental questions, including that of how different disordered phases ("polyamorphs") can coexist and transform from one to another. As a widely studied case, amorphous silicon (a-Si) forms a fourfold-coordinated, covalent random network at ambient conditions, but much higher-coordinated, metallic-like phases under pressure. However, a detailed mechanistic understanding of the liquid-amorphous and amorphous-amorphous transitions in silicon has been lacking, due to intrinsic limitations of even the most advanced experimental and computational techniques. Here, we show how machine-learning (ML)-driven simulations can break through this long-standing barrier, affording a comprehensive, quantum-accurate, and fully atomistic description of all relevant liquid and amorphous phases of silicon. Combining a model system size of 100,000 atoms (ten-nanometre length scale) with a prediction accuracy of a few meV per atom, our simulations reveal a remarkable, three-step transformation sequence for a-Si under increasing external pressure. First, up to 10-11 GPa, polyamorphic low- and high-density amorphous (LDA and HDA) regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct, very-high-density amorphous (VHDA) phase at 12-13 GPa, reminiscent of the dense liquid but being formed at a much lower temperature. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites at 13-16 GPa, ultimately leading to the formation of a poly-crystalline, simple-hexagonal structure, consistent with experiments but not seen in earlier simulations.

cond-mat.mtrl-sci↗

arXiv2019

Disorder by design: A data-driven approach to amorphous semiconductors without total-energy functionals

This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multi-objective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects ($\le$ 1%), a narrow bond-angle distribution of width 9-11.5 degree, and an electronic gap of 0.8-1.4 eV. These data-driven information-based models have been found to produce electronic and vibrational properties of amorphous silicon that match accurately with experimental data and rival that of the Wooten-Winer-Weaire (W3) models. The study confirms the effectiveness of a multi-objective optimization approach to the structural determination of complex materials, and resolves a long-standing dispute concerning the uniqueness of a model of tetrahedral amorphous semiconductors obtained via inversion of diffraction data.

cond-mat.dis-nn↗

arXiv2019

Temperature-induced nanostructural evolution of hydrogen-rich voids in amorphous silicon: A first-principles study

The paper presents an $ab$ $initio$ study of temperature-induced nanostructural evolution of hydrogen-rich voids in amorphous silicon. By using large $a$-Si models, obtained from classical molecular-dynamics simulations, with a realistic void-volume density of 0.2%, the dynamics of Si and H atoms on the surface of the nanometer-size cavities were studied and their effects on the shape and size of the voids were examined using first-principles density-functional simulations. The results from $ab$ $initio$ calculations were compared with those obtained from using the modified Stillinger-Weber potential. The temperature-induced nanostructural evolution of the voids was examined by analyzing the three-dimensional distribution of Si and H atoms on/near void surfaces using the convex-hull approximation, and computing the radius of gyration of the corresponding convex hulls. A comparison of the results with those from the simulated values of the intensity in small-angle X-ray scattering of $a$-Si/$a$-Si:H in the Guinier approximation is also provided, along with a discussion on the dynamics of bonded and non-bonded hydrogen in the vicinity of voids.

cond-mat.mtrl-sci↗

arXiv2019

A first-principles study on the lattice thermal conductivity of irradiated glassy states of the Ge$_2$Sb$_2$Te$_5$ phase-change memory material

An analysis of thermal transients from non-equilibrium ab initio molecular-dynamics simulations can be used to calculate the thermal conductivity of materials with a short phonon mean-free path. We adapt the approach-to-equilibrium methodology to the three-dimensional case of a simulation that consists of a cubic core region at higher temperature approaching thermal equilibrium with a thermostatted boundary. This leads to estimates of the lattice thermal conductivity for the glassy state of the phase-change memory material, Ge$_2$Sb$_2$Te$_5$, which are close to previously reported experimental measurements. Self-atom irradiation of the material, modelled using thermal spikes and stochastic-boundary conditions, results in glassy models with a significant reduction of the lattice thermal conductivity compared to the pristine glassy structure. This approach may prove to be useful in technological applications, e.g. for the suppression of thermal cross-talk in phase-change memory and data-storage devices.

cond-mat.mtrl-sci↗

arXiv2018

Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). Based on a similarity function ("kernel"), we define a structural metric that unifies the description of nearest- and next-nearest-neighbor environments in the amorphous state. We apply this to an ensemble of a-Si networks, generated in melt-quench simulations with an ML-based interatomic potential, in which we tailor the degree of ordering by varying the quench rates down to $10^{10}$ K/s (leading to a structural model that is lower in energy than the established WWW network). We then show how "machine-learned" atomic energies permit a chemical interpretation, associating coordination defects in a-Si with distinct energetic stability regions. The approach is straightforward and inexpensive to apply to arbitrary structural models, and it is therefore expected to have more general significance for developing a quantitative understanding of the amorphous state.

cond-mat.mtrl-sci↗

arXiv2018

Small-angle X-ray scattering in amorphous silicon: A computational study

We present a computational study of small-angle X-ray scattering (SAXS) in amorphous silicon ($a$-Si) with particular emphasis on the morphology and microstructure of voids. The relationship between the scattering intensity in SAXS and the three-dimensional structure of nanoscale inhomogeneities or voids is addressed by generating ultra-large high-quality $a$-Si networks with 0.1-0.3% volume concentration of voids, as observed in experiments using SAXS and positron annihilation spectroscopy. A systematic study of the variation of the scattering intensity in the small-angle scattering region with the size, shape, number density, and the spatial distribution of the voids in the networks is presented. Our results suggest that the scattering intensity in the small-angle region is particularly sensitive to the size and the total volume-fraction of the voids, but the effect of the geometry or shape of the voids is less pronounced in the intensity profiles. A comparison of the average size of the voids obtained from the simulated values of the intensity, using the Guinier approximation and Kratky plots, with those from the spatial distribution of the atoms in the vicinity of void surfaces is presented.

cond-mat.dis-nn↗

arXiv2018

Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of machine-learning algorithms to create interatomic potentials. Our best a-Si network is obtained by cooling from the melt in molecular-dynamics simulations, at a rate of 10$^{11}$ K/s (that is, on the 10 ns timescale). This structure shows a defect concentration of below 2% and agrees with experiments regarding excess energies, diffraction data, as well as $^{29}$Si solid-state NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning interatomic potentials for elucidating accurate structures and properties of amorphous functional materials.

cond-mat.mtrl-sci↗

arXiv2018

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density-functional theory (DFT) data. Rather than treating the full Li--C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li--Li interactions, to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials, and in the longer run is promising for carrying out detailed atomistic studies of battery materials.

cond-mat.mtrl-sci↗

arXiv2014

Electrical properties of Bi-implanted amorphous chalcogenide films

The impact of Bi implantation on the conductivity and the thermopower of amorphous chalcogenide films is investigated. Incorporation of Bi in Ge-Sb-Te and GeTe results in enhanced conductivity. The negative Seebeck coefficient confirms onset of the electron conductivity in GeTe implanted with Bi at a dose of 2x1016 cm-2. The enhanced conductivity is accompanied by defect accumulation in the films upon implantation as is inferred by using analysis of the space-charge limited current. The results indicate that native coordination defects in lone-pair semiconductors can be deactivated by means of ion implantation, and higher conductivity of the films stems from additional electrically active defects created by implantation of bismuth.

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