Reduction of Activation Energy Barrier of Stone-Wales Transformation in Endohedral Metallofullerenes

We examine effects of encapsulated metal atoms inside a C$_{60}$ molecule on the activation energy barrier to the Stone-Wales transformation using {\it ab initio} calculations. The encapsulated metal atoms we study are K, Ca and La which nominally donate one, two and three electrons to the C$_{60}$ cage, respectively. We find that isomerization of the endohedral metallofullerene via the Stone-Wales transformation can occur more easily than that of the empty fullerene owing to the charge transfer. When K, Ca and La atoms are encapsulated inside the fullerene, the activation energy barriers are lowered by 0.30, 0.55 and 0.80 eV, respectively compared with that of the empty C$_{60}$ (7.16 eV). The lower activation energy barrier of the Stone-Wales transformation implies the higher probability of isomerization and coalescence of metallofullerenes, which require a series of Stone-Wales transformations.Comment: 13 pages, 3 figures, 1 tabl

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.Comment: 3 figure

Disorder-dependent Li diffusion in Li6PS5Cl\mathrm{Li_6PS_5Cl} investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.Comment: 34 pages, 6 figure

GW Calculations on post-transition-metal oxides

In order to establish the reliable GW scheme that can be consistently applied to post-transition-metal oxides (post-TMOs), we carry out comprehensive GW calculations on electronic structures of ZnO, Ga2O3, In2O3, and SnO2, the four representative post-TMOs. Various levels of self-consistency (G0W0, GW0, and QPGW0) and different starting functionals (GGA, GGA + U, and hybrid functional) are tested and their influence on the resulting electronic structure is closely analyzed. It is found that the GW0 scheme with GGA + U as the initial functional turns out to give the best agreement with experiment, implying that describing the position of metal-d level precisely in the ground state plays a critical role for the accurate dielectric property and quasiparticle band gap. Nevertheless, the computation on ZnO still suffers from the shallow Zn-d level and we propose a modified approach (GW0+Ud) that additionally considers an effective Hubbard U term during GW0 iterations and thereby significantly improves the band gap. It is also shown that a GGA + U-based GW0(+Ud) scheme produces an accurate energy gap of crystalline InGaZnO4, implying that this can serve as a standard scheme that can be applied to general structures of post-TMOs. © 2014 American Physical Society.1991sciescopu

Investigation of the mechanism of the anomalous Hall effects in Cr2Te3/(BiSb)2(TeSe)3 heterostructure

The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the so-called topological Hall effect (THE) featuring a hump structure in the curve of the Hall resistance (Rxy) vs. a magnetic field (H) of a heterostructure consisting of a ferromagnet (FM) and a topological insulator (TI). The origin of the hump structure is still controversial between the topological Hall effect model and the multi-component anomalous Hall effect (AHE) model. In this work, we have investigated a heterostructure consisting of BixSb2-xTeySe3-y (BSTS) and Cr2Te3 (CT), which are well-known TI and two-dimensional FM, respectively. By using the so-called minor-loop measurement, we have found that the hump structure observed in the CT/BSTS is more likely to originate from two AHE channels. Moreover, by analyzing the scaling behavior of each amplitude of two AHE with the longitudinal resistivities of CT and BSTS, we have found that one AHE is attributed to the extrinsic contribution of CT while the other is due to the intrinsic contribution of BSTS. It implies that the proximity-induced ferromagnetic layer inside BSTS serves as a source of the intrinsic AHE, resulting in the hump structure explained by the two AHE model

Interaction and ordering of vacancy defects in NiO

By using a first-principles method employing the local density approximation plus Hubbard parameter approach, we study point defects in NiO and interactions between them. The defect states associated with nickel or oxygen vacancies are identified within the energy gap. It is found that nickel vacancies introduce shallow levels in the density of states for the spin direction opposite to that of the removed Ni atom, while the oxygen vacancy creates more localized in-gap states. The interaction profiles between vacancies indicate that specific defect arrangements are strongly favored for both nickel and oxygen vacancies. In the case of nickel vacancies, defect ordering in a simple-cubic style is found to be most stable, leading to a half-metallic behavior. The ionized oxygen vacancies also show a tendency toward clustering, more strongly than neutral pairs. The microscopic origin of vacancy clustering is understood based on overlap integrals between defect states. © 2008 The American Physical Society.open343