arXiv subjects

Richard Beanland

Publications and source records attributed to Richard Beanland.

At least 19 recordsLinked to original sources

arXiv2026

Resolving competing distortions in Ca0.4Sr0.6TiO3 using complementary electron and X-ray techniques

We present a study of the perovskite Ca0.4Sr0.6TiO3 using variable temperature transmission electron microscopy (TEM) and powder X-ray diffraction (PXRD). At room temperature and below, PXRD shows that the material adopts an orthorhombic Pbcm structure analogous to the P-phase of NaNbO3. Above 380 K the material transforms to a tetragonal I4/mcm phase. The structural distortions of these phases can be described as a combination of modes and order parameters associated with the M, T, $Δ$ and R-points of the Brillouin zone, each of which can be associated with a different set of superstructure reflections visible in X-ray and electron diffraction patterns. For the I4/mcm phase only the expected R-point reflections are observed in PXRD while both M and R- reflections are observed in electron diffraction. Using $Δ$ and R dark field TEM images we show that the phase transition proceeds by a loss of coherence of TiO6 octahedral tilting along the c-axis, leading to a microstructure of thin nanoscale platelets with a different local symmetry to the macroscopic structure. These persist well above the phase transition temperature and are probably responsible for the long-standing discrepancy between Raman spectroscopy and diffraction measurements in this materials system, as well as other secondary effects.

cond-mat.mtrl-sci↗

arXiv2026

Accurate and Efficient Interatomic Potentials for Dislocations in InP

We present Atomic Cluster Expansion (ACE) and MACE models trained on a new dataset of Density Functional Theory (DFT) calculations, constructed for the task of studying the mobility of dislocations in Indium Phosphide (InP). The models are validated in a suite of tests against RSCAN DFT, and compared with previously published potentials from literature. Our new models act as much better surrogates for DFT than the literature models: errors on partial dislocation formation energies are at most 4% for both ACE and MACE, compared with 18% for the MACE-MPA foundation model and 42-50% for earlier bespoke potentials. The bespoke MACE model achieves this accuracy while being around five times faster to evaluate than the MP0 and MPA foundation models.

cond-mat.mtrl-sci↗

arXiv2025

The Microscopic Structure of Stacking Faults in Sr$_2$NaNb$_5$O$_{15}$

Stacking faults and other topological defects in ferroics can have a significant influence on the electronic and mechanical properties of the material. Here, regular stacking faults in the tetragonal tungsten bronze material Sr$_2$NaNb$_5$O$_{15}$ are investigated through transmission electron microscopy, symmetry mode analysis and machine-learned force-field calculations. It is shown that the faults, with a fault vector of $\frac{1}{4}[\bar{2}12]_o$, annihilate in sets of four in the material, owing to the $\frac{1}{4}$ unit cell displacement along the b-axis. The four resulting domains emerge as four possible directions of the S$_3$ order parameter, related to NbO$_6$ octahedral tilts in the material. Force-field calculations reveal that the stacking faults are likely placed at positions where the octahedra in neighbouring domains have similar magnitudes of rotation, and that the estimated stacking fault energy is 46 mJ/m$^2$. The investigation shows that the stacking faults have a significant local effect on the polar modes present in the structure, and therefore could affect the ferroelectric properties.

cond-mat.mtrl-sci↗

arXiv2025

Template masks for 4D-STEM

We present a new analysis method for atomic resolution four-dimensional scanning transmission electron microscopy (4D-STEM, in which a diffraction pattern is collected at each point of a raster scan of a focused electron beam across the specimen). In 4D-STEM, each measured intensity has a dual character, forming a pixel in a diffraction pattern and, equally, forming a pixel in a STEM image. Applying a mask to the data to obtain a "virtual" bright field or dark field image is widely used and understood. However, there is a complementary procedure, in which an image (template) is applied to the data to obtain a mask. This mask shows the correlation between the data and the template and, when applied to atomic resolution 4D-STEM data produces an image optimised for the template. This allows, for example, imaging of specific atom columns and is a significant improvement over user-defined masks such as virtual annular bright field imaging. We demonstrate the capability of the approach, separately imaging Li and O atom columns in LiFePO4 and O, Pb and Ti across a domain wall in PbTiO3.These template masks provide a computationally straightforward and general method to probe 4D-STEM data. They are particularly effective for specimens of moderate thickness where multiple scattering produces strong and specific correlations in diffraction patterns.

physics.ins-det↗

arXiv2025

Pyramidal charged domain walls in ferroelectric BiFeO$_3$

Domain structures play a crucial role in the electric, mechanical and other properties of ferroelectric materials. In this study, we uncover the physical origins of the enigmatic zigzag domain structure in the prototypical multiferroic material BiFeO$_3$. Using phase-field simulations within the Landau-Ginzburg-Devonshire framework, we demonstrate that spatially-homogeneous defect charges result in domain structures that closely resemble those observed experimentally. The acquired understanding of the underlying physics of pyramidal-domain formation may enable the engineering of new materials with self-assembled domain structures exhibiting defined domain periodicity at the nanometre scale, opening avenues for advanced applications.

cond-mat.mtrl-sci↗

arXiv2025

Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks

We show how generative machine learning can be used for the rapid computation of strongly dynamical electron diffraction directly from crystal structures, specifically in large-angle convergent-beam electron diffraction (LACBED) patterns. We find that a conditional generative adversarial network can learn the connection between the projected potential from a cubic crystal's unit cell and the corresponding LACBED pattern. Our model can generate diffraction patterns on a GPU many orders of magnitude faster than existing direct simulation methods. Furthermore, our approach can accurately retrieve the projected potential from diffraction patterns, opening a new approach for the inverse problem of determining crystal structure.

cond-mat.mtrl-sci↗

arXiv2025

Superstructure reflexions in tilted perovskites part 2

In a previous article (Beanland & Sjokvist, 2024), we derived Boolean conditions for the appearance of superstructure reflexions in diffraction patterns from perovskites with tilted oxygen octahedra, using the structure factor equation. Assuming that the deviation from the untilted prototype perovskite structure was infinitesimally small, we expanded the structure factor as a Taylor series, truncated at the first term. This gave an elegant and simple method giving conditions on the presence or absence of superstructure reflexions. However, in real perovskite materials these distortions are sufficiently large for higher order terms to become significant, giving an additional set of superstructure reflexions. Here, we consider the second term in the expansion and show that it gives rise to second order reflexions with indices of the form half-even-even-odd. Boolean conditions for their presence are given.

cond-mat.mtrl-sci↗

arXiv2025

Superstructure reflexions in tilted perovskites Part 1

The superstructure spots that appear in diffraction patterns of tilted perovskites are well documented and easily calculated using crystallographic software. Here, by considering a distortion mode as a perturbation of the prototype perovskite structure, we show how the structure factor equation yields Boolean conditions for the presence of first order superstructure reflexions. A subsequent article describes conditions for second order reflexions, which appear only in structures with mixed in-phase and anti-phase oxygen octahedral tilting. This approach may have some advantages for the analysis of electron diffraction patterns of perovskites.

cond-mat.mtrl-sci↗

arXiv2022

Modelling fine-sliced three dimensional electron diffraction data with dynamical Bloch-wave simulations

Recent interest in structure solution and refinement using electron diffraction (ED) has been fuelled by its inherent advantages when applied to crystals of sub-micron size, as well as a better sensitivity to light elements. Currently, data is often processed using software written for X-ray diffraction, using the kinematic theory of diffraction to generate model intensities -- despite the inherent differences in diffraction processes in ED. Here, we use dynamical Bloch-wave simulations to model continuous rotation electron diffraction data, collected with a fine angular resolution (crystal orientations of $\sim0.1^\circ$). This fine-sliced data allows us to reexamine the corrections applied to ED data. We propose a new method for optimising crystal orientation, and take into account the angular range of the incident beam and varying slew rate. We extract observed integrated intensities and perform accurate comparisons with simulations using rocking curves for a (110) lamella of silicon 185~nm in thickness. $R_1$ is reduced from $26\%$ with the kinematic model to $6.8\%$ using dynamical simulations.

physics.comp-ph↗

arXiv2022

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and are shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in the manuscript is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

cond-mat.mtrl-sci↗

arXiv2021

Lateral electrodeposition of MoS2 semiconductor over an insulator

Developing novel techniques for depositing transition metal dichalcogenides is crucial for the industrial adoption of 2D materials in optoelectronics. In this work, the lateral growth of molybdenum disulfide (MoS2) over an insulating surface is demonstrated using electrochemical deposition. By fabricating a new type of microelectrodes, MoS2 2D films grown from TiN electrodes across opposite sides have been connected over an insulating substrate, hence, forming a lateral device structure through only one lithography and deposition step. Using a variety of characterization techniques, the growth rate of MoS2 has been shown to be highly anisotropic with lateral to vertical growth ratios exceeding 20-fold. Electronic and photo-response measurements on the device structures demonstrate that the electrodeposited MoS2 layers behave like semiconductors, confirming their potential for photodetection applications. This lateral growth technique paves the way towards room temperature, scalable and site-selective production of various transition metal dichalcogenides and their lateral heterostructures for 2D materials-based fabricated devices.

physics.app-ph↗

arXiv2021

Electrodeposited WS$_2$ Monolayers on Fabricated Graphene Electrodes

The development of scalable techniques to make 2D material heterostructures is a major obstacle that needs to be overcome before these materials can be implemented in device technologies industrially. Electrodeposition is an industrially compatible deposition technique that offers unique advantages in scaling 2D heterostructures. In this work, we demonstrate the electrodeposition of atomic layers of WS$_2$ over graphene electrodes using a single source precursor. Using conventional microfabrication techniques, graphene was patterned to create micro-electrodes where WS$_2$ was site-selectively deposited to form 2D heterostructures. We used various characterisation techniques, including atomic force microscopy, transmission electron microscopy, Raman spectroscopy and x-ray photoelectron spectroscopy to show that our electrodeposited WS$_2$ layers are highly uniform and can be grown over graphene at a controllable deposition rate. This technique to selectively deposit TMDCs over microfabricated graphene electrodes paves the way towards wafer-scale production of 2D material heterostructures for nanodevice applications.

physics.app-ph↗

arXiv2021

Phase Change Memory by GeSbTe Electrodeposition in Crossbar Arrays

Phase change memories (PCM) is an emerging type of non-volatile memory that has shown a strong presence in the data-storage market. This technology has recently attracted significant research interest in the development of non-Von Neumann computing architectures such as in-memory and neuromorphic computing. Research in these areas has been primarily motivated by the scalability potential of phase change materials and their compatibility with industrial nanofabrication processes. In this work, we are presenting our development of crossbar phase change memory arrays through the electrodeposition of GeSbTe (GST). We show that GST can be electrodeposited in microfabricated TiN crossbar arrays using a scalable process. Our phase switching test of the electrodeposited materials have shown that a SET/RESET resistance ratio of 2-3 orders of magnitude is achievable with a switching endurance of around 80 cycles. These results represent the first phase switching of electrodeposited GeSbTe in microfabricated crossbar arrays. Our work paves the way towards developing large memory arrays involving electrodeposited materials for passive selectors and phase switching devices. It also opens opportunities for developing a variety of different electronic devices using electrodeposited materials.

cond-mat.mtrl-sci↗

arXiv2020

Large-Area Electrodeposition of Few-Layer MoS2 on Graphene for 2D Material Heterostructures

Heterostructures involving two-dimensional (2D) transition metal dichalcogenides and other materials such as graphene have a strong potential to be the fundamental building block of many electronic and opto-electronic applications. The integration and scalable fabrication of such heterostructures is of essence in unleashing the potential of these materials in new technologies. For the first time, we demonstrate the growth of few-layer MoS2 films on graphene via non-aqueous electrodeposition. Through methods such as scanning and transmission electron microscopy, atomic force microscopy, Raman spectroscopy, energy and wavelength dispersive X-ray spectroscopies and X-ray photoelectron spectroscopy, we show that this deposition method can produce large-area MoS2 films with high quality and uniformity over graphene. We reveal the potential of these heterostructures by measuring the photo-induced current through the film. These results pave the way towards developing the electrodeposition method for the large-scale growth of heterostructures consisting of varying 2D materials for many applications.

physics.app-ph↗

arXiv2020

Characterizing oxygen atoms in perovskite and pyrochlore oxides using ADF-STEM at a resolution of a few tens of picometers

We present an aberration corrected scanning transmission electron microscopy (ac-STEM) analysis of the perovskite (LaFeO3) and pyrochlore (Yb2Ti2O7 and Pr2Zr2O7) oxides and demonstrate that both the shape and contrast of visible atomic columns in annular dark-field (ADF) images are sensitive to the presence of nearby atoms of low atomic number (e.g. oxygen). We show that point defects (e.g. oxygen vacancies), which are invisible - or difficult to observe due to limited sensitivity - in X-ray and neutron diffraction measurements, are the origin of the complex magnetic ground state of pyrochlore oxides. In addition, we present, for the first time, a method by which light atoms can be resolved in quantitative ADF-STEM images. Using this method, we resolved oxygen atoms in perovskite and pyrochlore oxides.

cond-mat.mtrl-sci↗

arXiv2020

Partial Scanning Transmission Electron Microscopy with Deep Learning

Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512$\times$512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9$\times$ with a 3.8\% test set root mean squared intensity error, and by 87.0$\times$ with a 6.2\% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models have been made publicly available at https://github.com/Jeffrey-Ede/partial-STEM

eess.IV↗

arXiv2020

Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning

Half of wavefunction information is undetected by conventional transmission electron microscopy (CTEM) as only the intensity, and not the phase, of an image is recorded. Following successful applications of deep learning to optical hologram phase recovery, we have developed neural networks to recover phases from CTEM intensities for new datasets containing 98340 exit wavefunctions. Wavefunctions were simulated with clTEM multislice propagation for 12789 materials from the Crystallography Open Database. Our networks can recover 224x224 wavefunctions in ~25 ms for a large range of physical hyperparameters and materials, and we demonstrate that performance improves as the distribution of wavefunctions is restricted. Phase recovery with deep learning overcomes the limitations of traditional methods: it is live, not susceptible to distortions, does not require microscope modification or multiple images, and can be applied to any imaging regime. This paper introduces multiple approaches to CTEM phase recovery with deep learning, and is intended to establish starting points to be improved upon by future research. Source code and links to our new datasets and pre-trained models are available at https://github.com/Jeffrey-Ede/one-shot

eess.IV↗

arXiv2019

Adaptive Learning Rate Clipping Stabilizes Learning

Artificial neural network training with stochastic gradient descent can be destabilized by "bad batches" with high losses. This is often problematic for training with small batch sizes, high order loss functions or unstably high learning rates. To stabilize learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated losses to a number of standard deviations above their running means. ALRC is designed to complement existing learning algorithms: Our algorithm is computationally inexpensive, can be applied to any loss function or batch size, is robust to hyperparameter choices and does not affect backpropagated gradient distributions. Experiments with CIFAR-10 supersampling show that ALCR decreases errors for unstable mean quartic error training while stable mean squared error training is unaffected. We also show that ALRC decreases unstable mean squared errors for partial scanning transmission electron micrograph completion. Our source code is publicly available at https://github.com/Jeffrey-Ede/ALRC

cs.LG↗