ShakeNBreak: Navigating the defect configurational landscape

Point defects are present in all crystalline solids, controlling the properties and performance of most functional materials, including thermoelectrics, photovoltaics and catalysts. However, the standard modelling approach, based on local optimisation of a defect placed on a known crystal site, can miss the true ground state structure. This structure may lie within a local minimum of the potential energy surface (PES), trapping a gradient-based optimisation algorithm in a metastable arrangement and thus yielding incorrect defect structures that compromise predicted properties (Mosquera-Lois & Kavanagh, 2021). As such, an efficient way to explore the defect energy landscape and identify low-energy structures is required

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

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