Alexey A. Sokol

Point defects in solid-state materials are conventionally modeled as lattice-derived defects, generated by adding or removing atoms from symmetric bulk lattice structures, followed by local geometry optimization. This approach often yields symmetric, weakly relaxed local minima that remain close to the parent lattice. Here, we show that this assumption can fail qualitatively for vacancies in many metal oxides. Rather than retaining a lattice-derived geometry with localized holes on oxygen sites, cation vacancies can drive extensive reconstruction, producing defect configurations in which the O–O bonds and O–O–O chains become the preferred charge-compensation motifs. We combined the minima hopping (MH) structure prediction algorithm with machine-learned interatomic potentials (MLIPs), hybrid QM/MM embedded-cluster calculations, and periodic density functional theory (DFT) calculations to investigate cation vacancies across a broad series of metal oxides. Our results show a clear oxidation-state-dependent trend. In the charge-neutral state, cation vacancies in oxides with formal oxidation states of +4 or higher favor reconstructed defect configurations containing O–O bonds or O–O–O chains with bond lengths of approximately 1.4 Å, lowering the defect formation energy by up to several electronvolts relative to the conventional structure. By contrast, in oxides with lower cation oxidation states, such reconstructions are generally unfavorable at charge neutrality but can become energetically more favorable at positive charge states. These findings define oxygen-bond formation as a general reconstruction pathway for cation vacancies in metal oxides and show that global structure searches are essential for predictive defect modeling beyond conventional lattice-derived local minima.

The influence of a copper substitution in an MgO lattice and the nature of hole states surrounding the dopant are investigated using hybrid quantum mechanical/molecular mechanical calculations at the density functional theory level alongside new Electron Paramagnetic Resonance (EPR) spectroscopic data on a synthesized sample of the material. In calculations, the neutral Cu(II)–MgO system displays Jahn–Teller distortion around the copper center in both tetragonally elongated and compressed configurations, in line with previous computational and experimental findings. Subsequently, the behavior of an electron hole is investigated in Cu(II)–MgO, which has been previously unexamined prior to this work. For this charged system, it is clearly shown that in the ground state a hole will delocalize across all coordinated oxygens. Localization of a hole on an oxygen species is possible but is found to be higher in energy, and a hybrid functional with an increased fraction of Hartree–Fock exchange is necessary. Notably, the calculations show that oxidation of the copper species to Cu(III) is not possible. The spin Hamiltonian parameters for the monoatomic copper centers in bulk MgO for both the neutral and ionized systems are also calculated and compared to experimental values obtained by EPR spectroscopy on a polycrystalline Cu(II)–MgO sample. The computed spin Hamiltonian parameters are shown to be largely consistent with the experimental parameters, and the axial distortion in the directional parameters in the material confirms that the copper dopant causes tetragonal elongation in its bonding with neighboring oxygen atoms.

QM/MM simulations show hydroxyl nest formation is exothermic in ZSM-5, FAU and quartz (CHA near-neutral), while subsequent proton rotation is favoured over inter-oxygen transfer, with a barrier nearly 40% lower (0.77 vs. 1.29 eV).

The exceptional performance of ceria (CeO₂) in catalysis and energy conversion is fundamentally governed by its defect chemistry, particularly oxygen vacancies. The formation of each oxygen vacancy (V_O^••) is assumed to be compensated by two localized electrons on cations (Ce³⁺). Here, we show by combining theory with experiment that while this 1 V_O^••: 2Ce³⁺ ratio accounts for the global charge compensation, it does not apply at the local scale, particularly in nanoparticles. Hybrid quantum mechanical/molecular mechanical (QM/MM) defect calculations, together with synchrotron X-ray photoelectron spectroscopy (XPS) measurements, show that electrons have a strong preference to localize and segregate on surfaces, which can overcome the trapping force from the V_O^•• sites in the bulk. At a given Fermi level, the surface V_O^•• tends to trap more electrons than those in bulk, resulting in a higher Ce³⁺ to V_O^•• ratio on surfaces than that in the bulk, driven by the preferential localization of electrons and enhanced V_O^••-Ce³⁺coupling. Large-scale unbiased Monte Carlo simulations on ceria nanoparticles confirmed this trend and further show that the surface segregation of electrons is more pronounced at low reduction levels and in smaller nanoparticles. In highly reduced ceria nanoparticles, however, the enhanced repulsive interactions lead to a less significant extent of defect heterogeneity or even reverse the location preference of defects in some nanoparticles. Our findings underscore the need to consider both the overall nonstoichiometry and local defect behavior in easily reducible oxides, with direct relevance to their performance in catalytic and energy applications.

Aluminum nitride (AlN), a wide-band-gap semiconductor, is a key material for ultraviolet optoelectronics and emerging quantum technologies. However, its performance is significantly affected by intrinsic point defects that introduce deep and shallow levels within the bandgap. Here, we combine a hybrid quantum mechanical/molecular mechanical embedded cluster approach with direct comparison to photoluminescence and cathodoluminescence measurements to investigate the optical transition properties of the four most stable intrinsic point defects in AlN: aluminum vacancies, nitrogen vacancies, aluminum interstitials, and nitrogen interstitials. Calculated configuration-coordinate diagrams across all relevant charge states enable the assignment of experimentally observed optical bands to specific defect-related transitions. Our results highlight N vacancies as the dominant contributors to optical signals under most growth conditions. Although we find that Al vacancies are frequently associated with blue-to-UV optical transitions, their actual contribution remains uncertain due to the dependence of their formation on donor-rich environments. N split-interstitials also play a significant role depending on concentrations and growth conditions. This theoretical analysis, grounded firmly in suitable experimental compaisons, advances the understanding of defect-related optical properties in AlN and offers guidance for the design of optoelectronic and quantum devices.

Dopant size is known to influence oxygen vacancy-mediated conduction pathways and ionic conductivity in doped ceria, yet the underlying atomic-scale mechanisms remain unclear. Here, we combine neutron total scattering and large-scale atomistic simulations to analyze the local defect structures of two representative doped ceria systems: Ce_0.8Gd_0.2O_1.9 (GDC) and Ce_0.8Nd_0.2O_1.9 (NDC). The local structure of GDC, a commercially used ion conductor, is investigated for the first time using neutron total scattering on ¹⁶⁰Gd-enriched samples. GDC exhibits fewer defect clusters, with vacancy pairs preferentially aligned along ⟨111⟩ and ⟨110⟩ directions while disfavoring ⟨100⟩ direction within the cubic fluorite structure. The Gd-Gd interactions in GDC help destabilize ⟨100⟩ ordering, promoting a more open defect network that supports efficient oxygen-ion transport. Unlike Gd³⁺ (1.053 Å in 8-fold coordination with oxygen), the slightly larger dopant Nd³⁺ (1.109 Å) in NDC promotes a more compact defect configuration, characterized by increased defect clustering and stabilized ⟨100⟩ vacancy alignment due to dominant Nd-vacancy interactions, substantially reducing ionic conductivity. Gd³⁺ provides an optimal balance of lattice expansion and preserving favorable defect structure for ion transport. These findings provide a mechanistic understanding of dopant-size controlled conduction pathways in lanthanide-doped ceria and fundamentally contribute to the understanding of charge transport by ions, electrons, and protons in next-generation conducting materials.

Testing the quality of MACE AI machine learnt potentials to model human-selected known silica structures and their behaviour. Silica materials are important as they have a wide range of mineralogical and industrial applications.

Alloys, solid solutions, and doped systems are essential in technologies such as energy generation and catalysis, but predicting their properties remains challenging because of compositional disorder. As the concentration of components changes in a binary solid solution A ( 1 − x ) B x , the number of possible configurations becomes computationally intractable. Algorithms used in classical optimization methods cannot avoid assessing high-energy states where, for example, simulated annealing is designed to initially spend computational effort. We introduce a scalable, practical, and accurate approach using quantum annealing to efficiently sample low-energy configurations of disordered materials, avoiding the need for excessive high-energy calculations. Our method includes temperature and simulates large unit cells, producing a Boltzmann-like distribution to identify thermodynamically relevant structures. We demonstrate this by predicting bandgap bowing in Al 1 − x Ga x N and bulk modulus variations in Ta 1 − x W x , with results in excellent agreement with experiments.

Formally charged double acceptors strontium vacancies prove to be the primary source of the hole excess over negative carriers supplied by donor species, contributing to the material's p-type conductivity.