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We have investigated the dynamics of Na ions in amorphous Na2Si2O5, a potential solid electrolyte material for Na-battery. We have employed quasielastic neutron scattering (QENS) technique in the amorphous Na2Si2O5 from 300 to 748 K to understand the diffusion pathways and relaxation timescales of Na atom dynamics. The microscopic analysis of the QENS data has been performed using ab-initio and classical molecular dynamics simulations (MD) to understand the Na-ion diffusion in the amorphous phase. Our experimental studies show that the traditional model, such as the Hall and Ross (H-R) model, fairly well describe the diffusion in the amorphous phase giving a mean jump length of ~3 {AA} and residence time about 9.1 picoseconds. Our MD simulations have indicated that the diffusion of Na+ ions occurs in the amorphous phase of Na2Si2O5 while that is not observed in the crystalline orthorhombic phase even up to 1100 K. The MD simulations have revealed that in the amorphous phase, due to different orientations of silicon polyhedral units, accessible pathways are opened up for Na+ diffusions. These pathways are not available in the crystalline phase of Na2Si2O5 due to rigid spatial arrangement of silicon polyhedral units.
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 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
We report the first measurements of the dynamics of liquid germanium (l-Ge) by quasi-elastic neutron scattering on time-of-flight and triple-axis spectrometers. These results are compared with simulation data of the structure and dynamics of l-Ge whi
By using molecular dynamics simulation, formation mechanisms of amorphous carbon in particular sp${}^3$ rich structure was researched. The problem that reactive empirical bond order potential cannot represent amorphous carbon properly was cleared in
Machine learning has been widely adopted to accelerate the screening of materials. Most existing studies implicitly assume that the training data are generated through a deterministic, unbiased process, but this assumption might not hold for the simu