TY - JOUR

T1 - DefAP

T2 - A Python code for the analysis of point defects in crystalline solids

AU - Neilson, W.D.

AU - Murphy, S.T.

PY - 2022/7/31

Y1 - 2022/7/31

N2 - Inevitably, all crystalline materials will contain imperfections that have the ability to modify the properties of the host material. Key to the development of advanced materials is the ability to predict the concentrations of different defects in any given environmental conditions and how the change in the defect population alters the material's properties. Modern first principles atomistic simulation techniques, such as density functional theory (DFT), are now widely employed for the simulation of point defects, however, to develop true insight into a material's defect chemistry, it is essential to link the energies calculated to thermodynamic variables that fully describe its operating conditions. The Defect Analysis Package (DefAP), an open-source Python code, has been designed to fulfil this role. The primary function of the package is to predict the concentrations of defects in materials as a function of key thermodynamic variables, such as temperature and availability of different species, expressed through chemical potentials. Through simple thermodynamic equations, DefAP allows the rapid exploration of a material's defect chemistry allowing direct comparison with experimental observations. Rapid exploration is supported through the use of autoplotting with carefully considered automatic data labelling and simplification options enabling production of publication quality plots. DefAP offers a wide range of options for the calculation of defect and carrier concentrations that can be customised by the user to suit the material studied and an extensive suite of options have been designed for the study of extrinsic defects (e.g. dopants or impurities). The capabilities of DefAP are demonstrated in this paper by studying intrinsic defects in YBa2Cu3O7, P doping in Si, Am accumulation in PuO2, and simultaneous build-up of T and He in Li2TiO3. © 2022 The Author(s)

AB - Inevitably, all crystalline materials will contain imperfections that have the ability to modify the properties of the host material. Key to the development of advanced materials is the ability to predict the concentrations of different defects in any given environmental conditions and how the change in the defect population alters the material's properties. Modern first principles atomistic simulation techniques, such as density functional theory (DFT), are now widely employed for the simulation of point defects, however, to develop true insight into a material's defect chemistry, it is essential to link the energies calculated to thermodynamic variables that fully describe its operating conditions. The Defect Analysis Package (DefAP), an open-source Python code, has been designed to fulfil this role. The primary function of the package is to predict the concentrations of defects in materials as a function of key thermodynamic variables, such as temperature and availability of different species, expressed through chemical potentials. Through simple thermodynamic equations, DefAP allows the rapid exploration of a material's defect chemistry allowing direct comparison with experimental observations. Rapid exploration is supported through the use of autoplotting with carefully considered automatic data labelling and simplification options enabling production of publication quality plots. DefAP offers a wide range of options for the calculation of defect and carrier concentrations that can be customised by the user to suit the material studied and an extensive suite of options have been designed for the study of extrinsic defects (e.g. dopants or impurities). The capabilities of DefAP are demonstrated in this paper by studying intrinsic defects in YBa2Cu3O7, P doping in Si, Am accumulation in PuO2, and simultaneous build-up of T and He in Li2TiO3. © 2022 The Author(s)

KW - Brouwer diagrams

KW - Defect chemistry

KW - Thermodynamics

U2 - 10.1016/j.commatsci.2022.111434

DO - 10.1016/j.commatsci.2022.111434

M3 - Journal article

VL - 210

JO - Computational Materials Science

JF - Computational Materials Science

SN - 0927-0256

M1 - 111434

ER -