ﻻ يوجد ملخص باللغة العربية
We revisit the color-diffusion algorithm [P. C. Aeberhard et al., Phys. Rev. Lett. 108, 095901 (2012)] in nonequilibrium ab initio molecular dynamics (NE-AIMD), and propose a simple efficient approach for the estimation of monovacancy jump rates in crystalline solids at temperatures well below melting. Color-diffusion applied to monovacancy migration entails that one lattice atom (colored-atom) is accelerated toward the neighboring defect-site by an external constant force F. Considering bcc molybdenum between 1000 and 2800 K as a model system, NE-AIMD results show that the colored-atom jump rate k_{NE} increases exponentially with the force intensity F, up to F values far beyond the linear-fitting regime employed previously. Using a simple model, we derive an analytical expression which reproduces the observed k_{NE}(F) dependence on F. Equilibrium rates extrapolated by NE-AIMD results are in excellent agreement with those of unconstrained dynamics. The gain in computational efficiency achieved with our approach increases rapidly with decreasing temperatures, and reaches a factor of four orders of magnitude at the lowest temperature considered in the present study.
We have performed quasielastic neutron scattering (QENS) experiments up to 1243 K and ab-initio molecular dynamics (AIMD) simulations to investigate the Na diffusion in various phases of NaAlSiO4 (NASO), namely, low-carnegieite (L-NASO; trigonal), hi
We present a comprehensive ab initio study of structural, electronic, lattice dynamical and electron-phonon coupling properties of the Bi(111) surface within density functional perturbation theory. Relativistic corrections due to spin-orbit coupling
We have performed quasielastic and inelastic neutron scattering (QENS and INS) measurements from 300 K to 1173 K to investigate the Na-diffusion and underlying host dynamics in Na2Ti3O7. The QENS data show that the Na atoms undergo localized jumps up
Available simulation methods, suitable to describe solid-solid phase transitions occurring upon increasing of presssure and/or temperature, are based on empirical interatomic potentials: this restriction reduces the predictive power, and thus the gen
Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data i