arXiv subjects

Irea Mosquera-Lois

Publications and source records attributed to Irea Mosquera-Lois.

8 recordsLinked to original sources

arXiv2026

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the semiconductor Sb2Se3. We introduce global defect charge embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict defect thermodynamics in quantitative agreement with direct quantum mechanical calculations, at a fraction of the computational cost.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Vacancy Levels in CsPbCl3 Obey Equilibrium Defect Thermodynamics

Halide vacancies are the dominant point defects in perovskites with VCl identified as a detrimental trap for the optoelectronic performance of CsPbCl3, with applications ranging from photodetectors to solar cells. Understanding these defects under operating conditions is key since their electronic levels exhibit large thermal fluctuations that challenge the validity of static 0 K models. However, quantitative modelling of defect processes requires hybrid density functional theory with spin-orbit coupling, which is too expensive for direct molecular dynamic simulations. To address this, we train a multi-task machine learning force field to study VCl in orthorhombic CsPbCl3 at 300 K. While we observe strong oscillations in the optical transition level arising from the soft potential energy surface, neither the non-radiative capture barriers nor the thermodynamic charge transition levels are affected. Our results reveal that VCl is not responsible for the non-radiative losses previously assumed. Instead, its impact on performance arises from other mechanisms, such as limiting the open-circuit voltage and promoting ionic migration. Our findings demonstrate that, despite strong dynamical effects in halide perovskites, the conventional static formalism of defect theory remains valid for predicting thermodynamic behavior, providing a sound basis for the design of high-performance energy materials.

cond-mat.mtrl-sci↗

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

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

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

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↗

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↗

arXiv2022

Identifying the ground state structures of point defects in solids

Point defects are a universal feature of crystalline materials. Their identification is often addressed by combining experimental measurements with theoretical models. The standard approach of simulating defects is, however, prone to missing the ground state atomic configurations associated with energy-lowering reconstructions from the idealised crystallographic environment. Missed ground states compromise the accuracy of calculated properties. To address this issue, we report an approach to efficiently navigate the defect configurational landscape using targeted bond distortions and rattling. Application of our workflow to a range of materials ($\rm CdTe$, $\rm GaAs$, $\rm Sb_2S_3$, $\rm Sb_2Se_3$, $\rm CeO_2$, $\rm In_2O_3$, $\rm ZnO$, anatase-$\rm TiO_2$) reveals symmetry breaking in each host crystal that is not found via conventional local minimisation techniques. The point defect distortions are classified by the associated physico-chemical factors. We demonstrate the impact of these defect distortions on derived properties, including formation energies, concentrations and charge transition levels. Our work presents a step forward for quantitative modelling of imperfect solids.

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