arXiv subjects

Keith T. Butler

Publications and source records attributed to Keith T. Butler.

At least 37 records · Page 2Linked to original sources

arXiv2022

Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. $\textit{Ab initio}$ models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from $\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and $\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available.

cond-mat.mtrl-sci↗

arXiv2022

Co-substituted BiFeO3: electronic, ferroelectric, and thermodynamic properties from first principles

Bismuth ferrite, BiFeO3, is a multiferroic solid that is attracting increasing attention as a potential photocatalytic material, because the ferroelectric polarisation enhances the separation of photogenerated carriers. With the motivation of finding routes to engineer the band gap and the band alignment, while conserving or enhancing the ferroelectric properties, we have investigated the thermodynamic, electronic and ferroelectric properties of BiCoxFe1 xO3 solid solutions, with 0 < x < 0.13, using density functional theory. We show that the band gap can be reduced from 2.9 eV to 2.1 eV by cobalt substitution, while simultaneously increasing the spontaneous polarisation, which is associated with a notably larger Born effective charge of Co compared to Fe cations. We discuss the interaction between Co impurities, which is strongly attractive and would drive the aggregation of Co, as evidenced by Monte Carlo simulations. Phase separation into a Co-rich phase is therefore predicted to be thermodynamically preferred, and the homogeneous solid solution can only exist in metastable form, protected by slow cation diffusion kinetics. Finally, we discuss the band alignment of pure and Co-substituted BiFeO3 with relevant redox potentials, in the context of its applicability in photocatalysis.

cond-mat.mtrl-sci↗

arXiv2022

Versatile Domain Mapping Of Scanning Electron Nanobeam Diffraction Datasets Utilising Variational AutoEncoders and Decoder-Assisted Latent-Space Clustering

Advancements in fast electron detectors have enabled the statistically significant sampling of crystal structures on the nanometre scale by means of Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural similarity across this length scale is key to bridging the gap between local atomic structure (using atomic resolution techniques such as High Resolution Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using bulk techniques such as powder X-ray and neutron diffraction). The use of SEND technique allows for structural investigation of a broad range of samples, due to the techniques ability to operate with low electron dosage and its tolerance for sample thickness, relative to HR-STEM. This, coupled with the capacity for data collection over a wide areas and the automation of this collection, allows for statistically representative sampling of the microstructure. Also due to these factors, SEND generates large datasets and as a result automated/ semi-automated data processing workflows are required to aid in maximal extraction of useful information. As such, this paper outlines a versatile, data-driven approach for producing domain maps, as well as a statistical approach for assessing their applicability. The production of such domain maps for a dataset can help highlight nuance in the microstructure, as well as improve the manageability of that dataset for further investigation. The workflow outlined utilises a Variational AutoEncoder to identify and learn the sources of variance in the diffraction signal and this, in combination with clustering techniques, is used to produce domain maps for a set of varied example cases. This approach: is agnostic to domain crystallinity; requires no prior knowledge of crystal structure; and does not require the, potentially prohibitive, simulation of a library of appropriate diffraction patterns.

cond-mat.mtrl-sci↗

arXiv2022

Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination of density functional theory and machine learning, using the cubic spinel nitride GeSn$_2$N$_4$ as a case study, exploring how formation energy and electronic bandgap are affected by configurational variations. Furthermore, we demonstrate the utility of applying explainable machine learning to understand the crystal chemistry origins of the trends that we observe. Different configuration descriptors (Coulomb matrix eigenspectrum, many-body tensor representation, and cluster correlation function vectors) are combined with different models (linear regression, gradient-boosted decision tree, and multi-layer perceptron) to extrapolate the calculation of ab initio properties from a small set of configurations to the full space with thousands of configurations. We discuss the performance of different descriptors and models. SHAP (SHapley Additive exPlanations) analysis of the machine learning models highlights how values of formation energy are dominated by variations in local crystal structure (single polyhedral environments), while values of electronic bandgap are dominated by variations in more extended structural motifs. Finally, we demonstrate the usefulness of this approach by constructing structure-property maps, identifying important configurations of GeSn$_2$N$_4$ with extremal properties, as well as by calculating accurate equilibrium properties using configurational averaging.

cond-mat.mtrl-sci↗

arXiv2021

Mixed-anion mixed-cation perovskite (FAPbI$_3$)$_{0.875}$(MAPbBr$_3$)$_{0.125}$: an ab-initio molecular dynamics study

Mixed-anion mixed-cation perovskites with (FAPbI$_3$)$_{1-x}$(MAPbBr$_3$)$_x$ composition have allowed record efficiencies in photovoltaic solar cells, but their atomic-scale behaviour is not well understood yet, in part because their theoretical modelling requires consideration of complex and interrelated dynamic and disordering effects. We present here an ab initio molecular dynamics investigation of the structural, thermodynamic, and electronic properties of the (FAPbI$_3$)$_{0.875}$(MAPbBr$_3$)$_{0.125}$ perovskite. A special quasi-random structure is proposed to mimic the disorder of both the molecular cations and the halide anions, in a stoichiometry that is close to that of one of today's most efficient perovskite solar cells. We show that the rotation of the organic cations is more strongly hindered in the mixed structure in comparison with the pure compounds. Our analysis suggests that this mixed perovskite is thermodynamically stable against phase separation despite the endothermic mixing enthalpy, due to the large configurational entropy. The electronic properties are investigated by hybrid density functional calculations including spin-orbit coupling in carefully selected representative configurations extracted from the molecular dynamics. Our model, that is validated here against experimental information, provides a more sophisticated understanding of the interplay between dynamic and disordering effects in this important family of photovoltaic materials.

cond-mat.mtrl-sci↗

arXiv2021

Ultralow Work Function of the Electride Sr$_3$CrN$_3$

Electrides have valence electrons that occupy free space in the crystal structure, making them easier to extract. This feature can be used in catalysis for important reactions that usually requires a high-temperature and high-pressure environments, such as ammonia synthesis. In this paper, we use density functional theory to investigate the behaviour of interstitial electrons of the 1-dimensional electride Sr$_3$CrN$_3$. We find that the bulk excess electron density persists on introduction of surface terminations, that the crystal termination perpendicular to the 1D free-electron channel is highly stable and we confirm an extremely low work function with hybrid functional methods. Our results indicate that Sr$_3$CrN$_3$ is a potentially important novel catalyst, with accessible, directional and extractable free electron density.

cond-mat.mtrl-sci↗

arXiv2021

Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties

Graph neural networks, trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks, once trained, are able to make highly accurate predictions at a fraction of the cost of experiments or first-principles calculations of comparable accuracy. However these networks typically rely on large databases of labelled experiments to train the model. In scenarios where data is scarce or expensive to obtain this can be prohibitive. By building a neural network that provides a confidence on the predicted properties, we are able to develop an active learning scheme that can reduce the amount of labelled data required, by identifying the areas of chemical space where the model is most uncertain. We present a scheme for coupling a graph neural network with a Gaussian process to featurise solid-state materials and predict properties \textit{including} a measure of confidence in the prediction. We then demonstrate that this scheme can be used in an active learning context to speed up the training of the model, by selecting the optimal next experiment for obtaining a data label. Our active learning scheme can double the rate at which the performance of the model on a test data set improves with additional data compared to choosing the next sample at random. This type of uncertainty quantification and active learning has the potential to open up new areas of materials science, where data are scarce and expensive to obtain, to the transformative power of graph neural networks.

cond-mat.mtrl-sci↗

arXiv2021

Distributed Representations of Atoms and Materials for Machine Learning

The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, via pooling operations of distributed representations of atoms. These compound representations are evaluated on ten different tasks, such as the prediction of formation energy and band gap, and are found to be competitive with existing benchmarks that make use of structure, and even superior in cases where only composition is available. Finally, we introduce a new approach for learning distributed representations of atoms, named SkipAtom, which makes use of the growing information in materials structure databases.

cond-mat.mtrl-sci↗

arXiv2021

Quantum-statistical transport phenomena in memristive computing architectures

The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has prevented widespread adoption. Here we show coherent electron wave functions play a pivotal role in the nanoscale transport properties of these emerging, non-volatile memories. By characterizing both filamentary and non-filamentary memristive devices as disordered Anderson systems, the switching characteristics and intrinsic variability arise directly from the universality of electron transport in disordered media. Our framework suggests localization phenomena in nanoscale, solid-state memristive systems are directly linked to circuit level performance. We discuss how quantum conductance fluctuations in the active layer set a lower bound on device variability. This finding implies there is a fundamental quantum limit on the reliability of memristive devices, and electron coherence will play a decisive role in surpassing or maintaining Moore's Law with these systems.

cond-mat.mes-hall↗

arXiv2020

Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data

Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and desired property. In the context of inelastic neutron scattering experiments, however, as in many other scientific scenarios, a number of issues arise: (i) scarcity of labelled experimental data, (ii) lack of uncertainty quantification on results, and (iii) lack of interpretability of the deep neural networks. In this work we examine approaches to all three issues. We use simulated data to train a deep neural network to distinguish between two possible magnetic exchange models of a half-doped manganite. We apply the recently developed deterministic uncertainty quantification method to provide error estimates for the classification, demonstrating in the process how important realistic representations of instrument resolution in the training data are for reliable estimates on experimental data. Finally we use class activation maps to determine which regions of the spectra are most important for the final classification result reached by the network.

cond-mat.mtrl-sci↗

arXiv2020

Modelling the dielectric constants of crystals using machine learning

The relative permittivity of a crystal is a fundamental property that links microscopic chemical bonding to macroscopic electromagnetic response. Multiple models, including analytical, numerical and statistical descriptions, have been made to understand and predict dielectric behaviour. Analytical models are often limited to a particular type of compounds, whereas machine learning (ML) models often lack interpretability. Here, we combine supervised ML, density functional perturbation theory, and analysis based on game theory to predict and explain the physical trends in optical dielectric constants of crystals. Two ML models, support vector regression and deep neural networks, were trained on a dataset of 1,364 dielectric constants. Shapley additive explanations (SHAP) analysis of the ML models reveals that they recover correlations described by textbook Clausius-Mossotti and Penn models, which gives confidence in their ability to describe physical behavior, while providing superior predictive power.

cond-mat.mtrl-sci↗

arXiv2019

Metal-free perovskites for non-linear optical materials

We identify the existence of nonlinear optical (NLO) activity in a number of novel $ABX_3$-type metal-free perovskites, where $A$ is a highly tuneable organic cation, $B$ is a NH$_4$ cation and $X$ a halide anion. Through systematic first-principles calculations, we identify important trends to chart the second-harmonic generation of this class of materials. We study three perovskites MDABCO-NH$_4$I$_3$, CNDABCO-NH$_4$I$_3$ and ODABCO-NH$_4$I$_3$ for use as deep-UV second-harmonic generation materials. We identify the role of the dipole moment imparted by the organic group on the $A$ cation as an important parameter to tune the NLO properties of these materials. We apply this knowledge functionalising the organic group DABCO with the highly polar cyanide CN$^-$ group, and we demonstrate a significant improvement of the NLO response in this family of materials. These findings can accelerate the application of metal free perovskites as inexpensive, non-toxic, earth-abundant materials for the next generation of optical communication applications.

cond-mat.mtrl-sci↗

arXiv2018

Finding a junction partner for candidate solar cell absorbers enargite and bournonite from electronic band and lattice matching

An essential step in the development of a new photovoltaic (PV) technology is choosing appropriate electron and hole extraction layers to make an efficient device. We recently proposed the minerals enargite (\enargite) and bournonite (\bournonite) as materials that are chemically stable with desirable optoelectronic properties for use as the absorber layer in a thin-film PV device. For these compounds, spontaneous lattice polarization with internal electric fields --- and potential ferroelectricity --- may allow for enhanced carrier separation and novel photophysical effects. In this work, we calculate the ionization potentials for non-polar surface terminations and propose suitable partners for forming solar cell heterojunctions by matching the electronic band edges to a set of candidate electrical contact materials. We then further screen these candidates by matching the lattice constants and identify those that are likely to minimise strain and achieve epitaxy. This two-step screening procedure identified a range of unconventional candidate contact materials including SnS2, ZnTe, WO3, and Bi2O3.

cond-mat.mtrl-sci↗

arXiv2018

Quick-start guide for first-principles modelling of semiconductor interfaces

Interfaces between dissimilar materials control the transport of energy in a range of technologies including solar cells (electron transport), batteries (ion transport), and thermoelectrics (heat transport). Advances in computer power and algorithms means that first-principles models of interfacial processes in realistic systems are now possible using accurate approaches such as density functional theory. In this `quick-start guide', we discuss the best practice in how to construct atomic models between two materials and analysis techniques appropriate to probe changes in local bonding and electronic band offsets. A number of examples are given related to perovskite solar cells.

cond-mat.mtrl-sci↗

arXiv2018

Band Engineering of Carbon Nitride Monolayers by N-type, P-type, and Isoelectronic Doping for Photocatalytic Applications

Since hydrogen fuel involves the highest energy density among all fuels, production of this gas through the solar water splitting approach has been suggested as a green remedy for greenhouse environmental issues due to extensive consumption of fossil fuels. Low dimensional materials possessing a large surface-to-volume ratio can be a promising candidate to be used for the photocatalytic approach. Here, we used extensive first principles calculations to investigate the application of newly fabricated members of two dimensional carbon nitrides including tg-C3N4, hg-C3N4, C2N, and C3N for water splitting. Band engineering via n-type, p-type, and isoelectronic doping agents such as B, N, P, Si, and Ge was demonstrated for tuning the electronic structure; optimizing solar absorption and band alignment for photocatalysis. Pristine tg-C3N4, hg-C3N4, and C2N crystals involve bandgaps of 3.190 eV, 2.772 eV, and 2.465 eV, respectively, which are not proper for water splitting. Among the dopants, Si and Ge dopants can narrow the band gap of carbon nitrides about 0.5 - 1.0 eV, and also increase their optical absorption in the visible spectrum. This study presents the potential for doping with isoelectronic elements to greatly improve the photocatalytic characteristics of carbon nitride nanostructures.

physics.comp-ph↗

arXiv2016

Microscopic origin of entropy-driven polymorphism in hybrid organic-inorganic perovskite materials

Entropy is a critical, but often overlooked, factor in determining the relative stabilities of crystal phases. The importance of entropy is most pronounced in softer materials, where small changes in free energy can drive phase transitions, which has recently been demonstrated in the case of organic-inorganic hybrid-formate perovskites. In this study we demonstrate the interplay between composition and crystal-structure that is responsible for the particularly pronounced role of entropy in determining polymorphism in hybrid organic-inorganic materials. Using ab initio based lattice dynamics we probe the origins and effects of vibrational entropy of four archetype perovskite (ABX$_3$) structures. We consider a fully inorganic material (SrTiO$_3$), an A-site hybrid halide material (CH$_3$NH$_3$PbI$_3$), an X-site hybrid material (KSr(BH$_4$)$_3$) and a mixed A- and X-site hybrid-formate material (N$_2$H$_5$Zn(HCO$_2$)$_3$), comparing the differences in entropy between two common polymorphs. The results demonstrate the importance of low-frequency inter-molecular modes in determining phase stability in these materials. The understanding gained allows us to propose a general principle for the relative stability of different polymorphs of hybrid materials as temperature is increased.

cond-mat.mtrl-sci↗

arXiv2015

Crystal structure optimisation using an auxiliary equation of state

Standard procedures for local crystal-structure optimisation involve numerous energy and force calculations. It is common to calculate an energy-volume curve, fitting an equation of state around the equilibrium cell volume. This is a computationally intensive process, in particular for low-symmetry crystal structures where each isochoric optimisation involves energy minimisation over many degrees of freedom. Such procedures can be prohibitive for non-local exchange-correlation functionals or other 'beyond' density functional theory electronic structure techniques, particularly where analytical gradients are not available. We present a simple approach for efficient optimisation of crystal structures based on a known equation of state. The equilibrium volume can be predicted from one single-point calculation, and refined with successive calculations if required. The approach is validated for PbS, PbTe, ZnS and ZnTe using nine density functionals, and applied to the quaternary semiconductor Cu$_{2}$ZnSnS$_{4}$ and the magnetic metal-organic framework HKUST-1.

cond-mat.mtrl-sci↗

arXiv2014

Ferroelectric Materials for Solar Energy Conversion: Photoferroics Revisited

The application of ferroelectric materials (i.e. solids that exhibit spontaneous electric polarisation) in solar cells has a long and controversial history. This includes the first observations of the anomalous photovoltaic effect (APE) and the bulk photovoltaic effect (BPE). The recent successful application of inorganic and hybrid perovskite structured materials (e.g. BiFeO3, CsSnI3, CH3NH3PbI3) in solar cells emphasises that polar semiconductors can be used in conventional photovoltaic architectures. We review developments in this field, with a particular emphasis on the materials known to display the APE/BPE (e.g. ZnS, CdTe, SbSI), and the theoretical explanation. Critical analysis is complemented with first-principles calculation of the underlying electronic structure. In addition to discussing the implications of a ferroelectric absorber layer, and the solid state theory of polarisation (Berry phase analysis), design principles and opportunities for high-efficiency ferroelectric photovoltaics are presented.

cond-mat.mtrl-sci↗