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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), high-carnegieite (H-NASO; cubic) and nepheline (N-NASO; hexagonal) phases. The QENS measurements reveal Na ions localized diffusion behavior in L-NASO and N-NASO, but long-range diffusion behavior in H-NASO. The AIMD simulation supplemented the QENS measurements and showed that excess Na ions in H-NASO enhance the host network flexibility and activate the AlO4/SiO4 tetrahedra rotational modes. These framework modes enable the long-range diffusion of Na across a pathway of interstitial sites. The simulations also show Na diffusion in Na-deficient N-NASO through vacant Na sites along the hexagonal c-axis.
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 to 1173 K. The ab-initio molecular dynamics (AIMD) simulations supplement the measurements and show 1-d long-ranged diffusion along the a-axis above 1500 K. The simulations indicate that the occupancy of the interstitial site is critical for long-range diffusion. The nudged-elastic-band (NEB) calculation confirmed that the activation energy barrier is lowest for diffusion along the a-axis. In the experimental phonon spectra the peaks at 10 and 14 meV are dominated by Na dynamics that disappear on warming, suggesting low-energy phonons significantly contribute to large Na vibrational amplitude at elevated temperatures that enhances the Na hopping probability. We have also calculated the mode Gruneisen parameters of the phonons and thereby calculated the volume thermal expansion coefficient, which is found to be in excellent agreement with available experimental data.
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 which have been obtained with ab initio density functional theory methods. The simulations accurately reproduce previous results from elastic and inelastic scattering experiments, as well as the q-dependence of the width of the quasi-elastic signal of the new experimental data. In order to understand some special features of the structure of the liquid we have also simulated amorphous Ge. Overall we find that the atomistic model represents accurately the average structure of real l-Ge as well as the time dependent structural fluctuations. The new quasi-elastic neutron scattering data allows us to investigate to what extent simple theoretical models can be used to describe diffusion in l-Ge.
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 the transition process from graphite to diamond by high pressure and the deposition process of amorphous carbon thin films. Moreover, the new potential model which is based on electron distribution simplified as a point charge was developed by using downfolding method. As a result, the molecular dynamics simulation with the new potential could demonstrate the transition from graphite to diamond at the pressure of 15 GPa corresponding to experiment and the deposition of sp${}^3$ rich amorphous carbon.
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 simulation of some complex materials. In this work, we aim to screen amorphous polymer electrolytes which are promising candidates for the next generation lithium-ion battery technology but extremely expensive to simulate due to their structural complexity. We demonstrate that a multi-task graph neural network can learn from a large amount of noisy, biased data and a small number of unbiased data and reduce both random and systematic errors in predicting the transport properties of polymer electrolytes. This observation allows us to achieve accurate predictions on the properties of complex materials by learning to reduce errors in the training data, instead of running repetitive, expensive simulations which is conventionally used to reduce simulation errors. With this approach, we screen a space of 6247 polymer electrolytes, orders of magnitude larger than previous computational studies. We also find a good extrapolation performance to the top polymers from a larger space of 53362 polymers and 31 experimentally-realized polymers. The strategy employed in this work may be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.