First-Principles Modelling of Ion-Substituted Hydroxyapatite: Defect Chemistry, Site Preferences, and Structure–Property Relationships—A Critical Review

Hydroxyapatite (HAp) possesses exceptional compositional flexibility, allowing for extensive cationic, anionic, and coupled substitutions that can modify its structural, mechanical, electronic, magnetic, catalytic, and biological properties. However, experimental identification of dopant locations and charge-compensation mechanisms remains challenging, particularly in nanocrystalline, calcium-deficient, and multiphase materials. This review evaluates first-principles studies of ion-substituted HAp, with particular emphasis on substitution-site preferences, defect association, charge compensation, thermodynamic stability, configurational effects, surfaces, hydration, and comparison with experimental observables. Unlike earlier reviews that primarily catalogued individual dopants and their calculated effects, the present work critically examines the methodological and defect-chemical assumptions underlying reported substitution mechanisms, including charge compensation, configurational sampling, chemical-potential definitions, hydration, finite-temperature effects, and experimental validation. The literature demonstrates that dopant site preference is not an intrinsic and concentration-independent property of an element. Instead, it depends on supercell size, dopant concentration, hydroxyl ordering, oxidation and spin states, compensating defects, chemical potentials, hydration, temperature, and the presence of other impurities. Many apparent discrepancies between studies arise from small simulation cells, incomplete configurational sampling, inconsistent formation-energy definitions, or calculations performed for assumed compositions without explicit charge compensation. The strongest structural assignments combine defect energetics with directly calculated spectroscopic parameters. Future predictive models should treat substituted HAp as a condition-dependent ensemble of interacting defects and integrate large-scale configurational sampling, finite-temperature methods, hydrated surfaces, machine learning, and advanced spectroscopic validation.