arXiv subjects

Aron Walsh

Publications and source records attributed to Aron Walsh.

At least 37 records · Page 2Linked to original sources

arXiv2024

Point defect formation at finite temperatures with machine learning force fields

Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change in Gibbs free energy is approximated by the internal energy. This approach has a low computational cost, but ignores contributions from atomic vibrations and structural configurations that can be accessed at finite temperatures. We train a machine learning force field (MLFF) to explore dynamic defect behaviour using $\mathrm{Te_i^{+1}}$ and $\textit{V}{\mathrm{_{Te}^{+2}}}$ in CdTe as exemplars. We consider the different entropic contributions (e.g., electronic, spin, vibrational, orientational, and configurational) and compare methods to compute the defect free energies, ranging from a harmonic treatment to a fully anharmonic approach based on thermodynamic integration. We find that metastable configurations are populated at room temperature and thermal effects increase the predicted concentration of $\mathrm{Te_i^{+1}}$ by two orders of magnitude -- and can thus significantly affect the predicted properties. Overall, our study underscores the importance of finite-temperature effects and the potential of MLFFs to model defect dynamics at both synthesis and device operating temperatures.

cond-mat.mtrl-sci↗

arXiv2024

Sulfur Vacancies Limit the Open-circuit Voltage of Sb2S3 Solar Cells

Antimony sulfide (Sb2S3) is a promising candidate as an absorber layer for single-junction solar cells and the top subcell in tandem solar cells. However, the power conversion efficiency of Sb2S3-based solar cells has remained stagnant over the past decade, largely due to trap-assisted non-radiative recombination. Here we assess the trap-limited conversion efficiency of Sb2S3 by investigating non-radiative carrier capture rates for intrinsic point defects using first-principles calculations and Sah-Shockley statistics. Our results show that sulfur vacancies act as effective recombination centers, limiting the maximum efficiency of Sb2S3 to 16% light to electricity. The equilibrium concentrations of sulfur vacancies remain relatively high regardless of growth conditions, indicating the intrinsic limitations imposed by these vacancies on the performance of Sb2S3.

cond-mat.mtrl-sci↗

arXiv2024

Multifaceted nature of defect tolerance in halide perovskites and emerging semiconductors

Lead-halide perovskites (LHPs) have shot to prominence as efficient energy conversion materials that can be processed using cost-effective fabrication methods. A widely-quoted reason for their exceptional performance is their ability to tolerate defects, enabling long charge-carrier lifetimes despite high defect densities. Realizing defect tolerance in broader classes of materials would have a substantial impact on the semiconductor industry. Significant effort has been made over the past decade to unravel the underlying origins of defect tolerance to design stable alternatives to LHPs comprised of nontoxic elements. However, it has become clear that understanding defect tolerance in LHPs is far from straightforward. This review discusses the models proposed for defect tolerance in halide perovskites, evaluating the experimental and theoretical support for these models, as well as their limitations. We cover attempts to apply these models to identify materials beyond the lead-halide system that could also exhibit defect tolerance, and the successes and pitfalls encountered over the past decade. Finally, a discussion is made of some of the important missing pieces of information required for a deeper understanding and predictive models that enable the inverse design of defect tolerant semiconductors.

cond-mat.mtrl-sci↗

arXiv2024

Picosecond lifetimes of hydrogen bonds in the halide perovskite CH$_3$NH$_3$PbBr$_3$

The structures and properties of organic-inorganic perovskites are influenced by the hydrogen bonding between the organic cations and the inorganic octahedral networks. This study explores the dynamics of hydrogen bonds in CH$_3$NH$_3$PbBr$_3$ across a temperature range from 70 K to 350 K, using molecular dynamics simulations with machine-learning force fields. The results indicate that the lifetime of hydrogen bonds decreases with increasing temperature from 7.6 ps (70 K) to 0.16 ps (350 K), exhibiting Arrhenius-type behaviour. The geometric conditions for hydrogen bonding, which include bond lengths and angles, maintain consistency across the full temperature range. The relevance of hydrogen bonds for the vibrational states of the material is also evidenced through a detailed analysis of the vibrational power spectra, demonstrating their significant effect on the physical properties for this class of perovskites.

cond-mat.mtrl-sci↗

arXiv2024

Multi-Objective Quality-Diversity for Crystal Structure Prediction

Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Prediction methods focus on identifying the most stable solutions that lie at the global minimum of the energy function. This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation. By contrast, Quality-Diversity algorithms provide a promising avenue for Crystal Structure Prediction as they aim to find a collection of high-performing solutions that have diverse characteristics. However, it may also be valuable to optimise for the stability of crystal structures alongside other objectives such as magnetism or thermoelectric efficiency. Therefore, in this work, we harness the power of Multi-Objective Quality-Diversity algorithms in order to find crystal structures which have diverse features and achieve different trade-offs of objectives. We analyse our approach on 5 crystal systems and demonstrate that it is not only able to re-discover known real-life structures, but also find promising new ones. Moreover, we propose a method for illuminating the objective space to gain an understanding of what trade-offs can be achieved.

cs.NE↗

arXiv2024

Dynamic Nanodomains Dictate Macroscopic Properties in Lead Halide Perovskites

Empirical A-site cation substitution has advanced the stability and efficiency of hybrid organic-inorganic lead halide perovskites solar cells and the functionality of X-ray detectors. Yet, the fundamental mechanisms underpinning their unique performance remain elusive. This multi-modal study unveils the link between nanoscale structural dynamics and macroscopic optoelectronic properties in these materials by utilising X-ray diffuse scattering, inelastic neutron spectroscopy and optical microscopy complemented by state-of-the-art machine learning-assisted molecular dynamics simulations. Our approach uncovers the presence of dynamic, lower-symmetry local nanodomains embedded within the higher-symmetry average phase in various perovskite compositions. The properties of these nanodomains are tunable via the A-site cation selection: methylammonium induces a high density of anisotropic, planar nanodomains of out-of-phase octahedral tilts, while formamidinium favours sparsely distributed isotropic, spherical nanodomains with in-phase tilting, even when crystallography reveals cubic symmetry on average. The observed variations in the properties of dynamic nanodomains are in agreement with our simulations and are directly linked to the differing macroscopic optoelectronic and ferroelastic behaviours of these compositions. By demonstrating the influence of A-site cation on local nanodomains and consequently, on macroscopic properties, we propose leveraging this relationship to engineer the optoelectronic response of these materials, propelling further advancements in perovskite-based photovoltaics, optoelectronics, and X-ray imaging.

cond-mat.mtrl-sci↗

arXiv2024

Anisotropic electron-phonon interactions in 2D lead-halide perovskites

Two-dimensional hybrid organic-inorganic metal halide perovskites offer enhanced stability for perovskite-based applications. Their crystal structure's soft and ionic nature gives rise to strong interactions between charge carriers and ionic rearrangements. Here, we investigate the interaction of photo-generated electrons and ionic polarizations in single-crystal 2D perovskite butylammonium lead iodide, varying the inorganic lammelae thickness in the 2D single crystals. We determined the directionality of the transition dipole moments of the relevant phonon modes (in the 0.3-3 THz range) by angle-and-polarization dependent THz transmission measurements. We find a clear anisotropy of the in-plane photoconductivity, with a 10% reduction along the axis parallel with the transition dipole moment of the most strongly coupled phonon. Detailed calculations, based on Feynman polaron theory, indicate that the anisotropy originates from directional electron-phonon interactions.

cond-mat.mtrl-sci↗

arXiv2024

doped: Python toolkit for robust and repeatable charged defect supercell calculations

Defects are a universal feature of crystalline solids, dictating the key properties and performance of many functional materials. Given their crucial importance yet inherent difficulty in measuring experimentally, computational methods (such as DFT and ML/classical force-fields) are widely used to predict defect behaviour at the atomic level and the resultant impact on macroscopic properties. Here we report doped, a Python package for the generation, pre-/post-processing, and analysis of defect supercell calculations. doped has been built to implement the defect simulation workflow in an efficient and user-friendly -- yet powerful and fully-flexible -- manner, with the goal of providing a robust general-purpose platform for conducting reproducible calculations of solid-state defect properties.

cond-mat.mtrl-sci↗

arXiv2024

Na Vacancy Driven Phase Transformation and Fast Ion Conduction in W-doped Na$_3$SbS$_4$ from Machine Learning Force Fields

Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na$_3$SbS$_4$ (Pn = P, Sb; Ch = S, Se) family have been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping, and its impact on diffusion and phase stability, we trained a machine learning force field for Na$_{3-x}$W$_{x}$Sb$_{1-x}$S$_4$ based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilises the global cubic phase at lower temperatures with enhanced Na ion diffusion, and that the explicit role of the substitutional W dopants is limited. In the global cubic phase we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials.

cond-mat.mtrl-sci↗

arXiv2024

Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms

The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} algorithms to the field of crystal structure prediction. The objective of these algorithms is to identify a diverse set of high-performing solutions, which has been successful in a range of fields such as robotics, architecture and aeronautical engineering. As these methods rely on a high number of evaluations, we employ machine-learning surrogate models to compute the interatomic potential and material properties that are used to guide optimisation. Consequently, we also show the value of using neural networks to model crystal properties and enable the identification of novel composition--structure combinations. In this work, we specifically study the application of the MAP-Elites algorithm to predict polymorphs of TiO$_2$. We rediscover the known ground state, in addition to a set of other polymorphs with distinct properties. We validate our method for C, SiO$_2$ and SiC systems, where we show that the algorithm can uncover multiple local minima with distinct electronic and mechanical properties.

cond-mat.mtrl-sci↗

arXiv2024

Upper efficiency limit of Sb2Se3 solar cells

Antimony selenide (Sb2Se3) is at the forefront of an emerging class of sustainable photovoltaic materials. Despite notable developments over the past decade, the light-to-electricity conversion efficiency of Sb2Se3 has reached a plateau of ~10%. Is this an intrinsic limitation of the material or is there scope to rival the success of metal halide perovskite solar cells? Here we assess the trap-limited conversion efficiency of Sb2Se3. First-principles defect analysis of the hole and electron capture rates for point defects demonstrates the critical role of vacancies as active recombination centres. We predict an upper limit of 25% efficiency in Sb2Se3 grown under optimal equilibrium conditions where the concentrations of charged vacancies are minimised. We further reveal how the detrimental effect of Se vacancies can be reduced by extrinsic oxygen passivation, highlighting a pathway to achieve high-performance metal selenide solar cells close to the thermodynamic limit.

cond-mat.mtrl-sci↗

arXiv2024

Machine-learning structural reconstructions for accelerated point defect calculations

Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the defect structural landscape. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSe$_x$Te$_{1-x}$ alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurational complex systems.

cond-mat.mtrl-sci↗

arXiv2024

Factors Enabling Delocalized Charge-Carriers in Pnictogen-Based Solar Absorbers: In-depth Investigation into CuSbSe2

Inorganic semiconductors based on heavy pnictogen cations (Sb3+ and Bi3+) have gained significant attention as potential nontoxic and stable alternatives to lead-halide perovskites for solar cell applications. A limitation of these novel materials, which is being increasingly commonly found, is carrier localization, which substantially reduces mobilities and diffusion lengths. Herein, the layered příbramite CuSbSe2 is investigated and discovered to have delocalized free carriers, as shown through optical pump terahertz probe spectroscopy and temperature-dependent mobility measurements. Using a combination of theory and experiment, it is found that the underlying factors are: 1) weak coupling to acoustic phonons due to low deformation potentials, as lattice distortions are primarily accommodated through rigid inter-layer movement rather than straining inter-atomic bonds, and 2) weak coupling to optical phonons due to the ionic contributions to the dielectric constant being low compared to electronic contributions. This work provides important insights into how pnictogen-based semiconductors avoiding carrier localization could be identified.

cond-mat.mtrl-sci↗

arXiv2023

Roadmap on Photovoltaic Absorber Materials for Sustainable Energy Conversion

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO 2 eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.

physics.app-ph↗

arXiv2023

Element similarity in high-dimensional materials representations

The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-structure-property relationships. Here, we assess the similarity and correlations contained within high-dimensional local and distributed representations of the chemical elements, as implemented in an open-source Python package ElementEmbeddings. These include element vectors of up to 200 dimensions derived from known physical properties, crystal structure analysis, natural language processing, and deep learning models. A range of distance measures are compared and a clustering of elements into familiar groups is found using dimensionality reduction techniques. The cosine similarity is used to assess the utility of these metrics for crystal structure prediction, showing that they can outperform the traditional radius ratio rules for the structural classification of AB binary solids.

cond-mat.mtrl-sci↗

arXiv2023

Structural Dynamics Descriptors for Metal Halide Perovskites

Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modelling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH$_3$NH$_3$PbBr$_3$) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI$_3$ based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions.

cond-mat.mtrl-sci↗

arXiv2023

Imperfections are not 0 K: free energy of point defects in crystals

Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware, and the integration of new computational techniques such as machine learning, provide an opportunity to model longer length and time-scales than previously possible. In this Tutorial Review, we cover the description of free energies for defect formation at finite temperatures, including configurational (structural, electronic, spin) and vibrational terms. We discuss challenges in accounting for metastable defect configurations, progress such as machine learning force fields and thermodynamic integration to directly access entropic contributions, and bottlenecks in going beyond the dilute limit of defect formation. Such developments are necessary to support a new era of accurate defect predictions in computational materials chemistry.

cond-mat.mtrl-sci↗

arXiv2023

Metal halide thermoelectrics: prediction of high-performance CsCu2I3

Thermoelectric devices can directly convert waste heat into electricity, which makes them an important clean energy technology. The underlying materials performance can be evaluated by the dimensionless figure of merit ZT. Metal halides are attractive candidates due to their chemical flexibility and ease of processing; however, the maximum ZT realized (ZT = 0.15) falls far below the level needed for commercialization (ZT > 1). Using a first-principles procedure we assess the thermoelectric potential of copper halide CsCu2I3, which features 1D Cu-I connectivity. The n-type crystal is predicted to exhibit a maximum ZT of 2.2 at 600 K along the b-axis. The strong phonon anharmonicity of this system is shown by locally stable non-centrosymmetric Amm2 structures that are averaged to form the observed centrosymmetric Cmcm space group. Our work provides insights into the structure-property relations in metal halide thermoelectrics and suggests a path forward to engineer higher-performance heat-to-electricity conversion.

cond-mat.mtrl-sci↗