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

Abstract

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.

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• Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics
• Instability of oxide perovskite surfaces induced by vacancy formation
• DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells
• Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
• Unveiling the Impact of Sulfur Doping on Copper-Substituted Lead Apatite: A Theoretical Study
• Thermodynamics of proton insertion across the perovskite-brownmillerite transition in La0.5Sr0.5CoO3-δ
• Accelerating point defect simulations using data-driven and machine learning approaches
• Origin of Bright Quantum Emissions with High Debye-Waller factor in Silicon Nitride
• Tailoring the Electronic Properties of Monoclinic (InxAl1-x)2O3 Alloys via Substitutional Donors and Acceptors