Electrostrictive materials have wide applications in modern high-precision electronic devices. Driven by growing environmental concerns, there is demand for lead-free materials with superior electrostriction behaviors. In this study, we demonstrate a record-high electrostrictive coefficient of ~0.0712 m 4 C À2 in perovskite ferroelectric ceramics, along with hysteresis-free strain as well as excellent frequency and thermal stabilities, in lead-free BaTiO 3 -based ceramics through a polarization nanocluster design. By appropriately introducing Li + and Bi 3+ into the BaTiO 3 lattice matrix, the longrange ferroelectric ordering can be broken, and polarization nanoclusters can be formed, resulting in a relaxor state with concurrently suppressed polarization and maintained electro-strain. A three-dimensional atomic model constructed using advanced neutron total-scattering data combined with the reverse Monte Carlo method indicates the existence of Bi and Li segregations at the subnanometer scale, which confirms the prediction made by density functional theory calculations. Such a short-range chemical order destroys the long-range ferroelectric order of the off-centered Ti polar displacements and leads to the embedding of Li + /Bi 3+ -rich polar nanoregions in the Ba 2+ -rich polarization disorder matrix. Further, a completely reversible electric-fieldinduced lattice strain is observed, giving rise to pure electrostriction without hysteresis behavior. This work provides a novel strategy for developing leadfree relaxor ferroelectrics with high electrostriction performance.
The search of ground-state structures (GSS) of gold (Au) clusters is a formidable challenge due to the complexity of potential energy surface (PES). In this work, we have built a high-dimensional artificial neural network (ANN) potential to describe the PES of Au20 clusters. The ANN potential is trained through learning the GSS search process of Au20 by the combination of density functional theory (DFT) method and genetic algorithm (GA). The root mean square errors (RMSE) of energy and force are 7.72 meV/atom and 217.02 meV/Å, respectively. As a result, it can find the lowest-energy structure (LES) of Au20 clusters that is consistent with previous results. Furthermore, the scalability test shows that it can predict the energy of smaller size Au16-19 clusters with errors less than 22.85 meV/atom, and for larger size Au21-25 clusters, the errors are below 36.94 meV/atom. Extra attention should be paid to its accuracy for Au21-25 clusters. Applying the ANN to search the GSS of Au16-25, we discover two new structures of Au16 and Au21 that are not reported before and several candidate LESs of Au16-18. In summary, this work proves that an ANN potential trained for specific size clusters could reproduce the GSS search process by DFT and be applied in the GSS search of smaller size clusters nearby. Therefore, we claim that building ANN potential based on DFT data is one of the most promising ways to effectively accelerate the GSS pre-screening of clusters.
In cluster science, it is challenging to identify the ground state structures (GSS) of gold (Au) clusters. Among different search approaches, first-principles method based on density functional theory (DFT) is the most reliable one with high precision. However, as the cluster size increases, it requires more expensive computational cost and becomes impracticable. In this paper, we have developed an artificial neural network (ANN) potential for Au clusters, which is trained to the DFT binding energies and forces of 9000 Au N clusters (11 ≤ N ≤ 100). The root mean square errors of energy and force are 13.4 meV/atom and 0.4 eV/Å, respectively. We demonstrate that the ANN potential has the capacity to differentiate the energy level of Au clusters and their isomers and highlight the need to further improve the accuracy. Given its excellent transferability, we emphasis that ANN potential is a promising tool to breakthrough computational bottleneck of DFT method and effectively accelerate the pre-screening of Au clusters’ GSS.
Pressure-temperature-volume (P-T-V) data on liquid iron-sulfur (Fe-S) alloys at the Earth's outer core conditions (~136 to 330 GPa, ~4000 to 7000 K) have been obtained by first-principles molecular dynamics simulations. We developed a thermal equation of state (EoS) composed of Murnaghan and Mie-Grüneisen-Debye expressions for liquid Fe-S alloys. The density and sound velocity are calculated and compared with Preliminary Reference Earth Model (PREM) to constrain the S concentration in the outer core. Since the temperature at the inner core boundary (TICB) has not been measured precisely (4850~7100 K), we deduce that the S concentration ranges from 10~14 wt% assuming S is the only light element. Our results also show that Fe-S alloys cannot satisfy the seismological density and sound velocity simultaneously and thus S element is not the only light element. Considering the geophysical and geochemical constraints, we propose that the outer core contains no more than 3.5 wt% S, 2.5 wt% O, or 3.8 wt% Si. In addition, the developed thermal EoS can be utilized to calculate the thermal properties of liquid Fe-S alloys, which may serve as the fundamental parameters to model the Earth's outer core.
In this study, it was demonstrated that Na+ ionic doped crystals could be formed during the solidification of FLiNaK melts. The ionic adulterating behavior between Li+, Na+, and K+ ions was investigated in detail by using various 1D/2D solid-state NMR methods and theoretical calculations. 2D 19F–23Na and 19F–7Li HETCOR NMR showed that the Na+ ions could be doped into LiF and KF crystals to generate new adulterating phases. Furthermore, the doped ionic Na+ ions exhibited different dynamics in LiF and KF crystals, which is correlated to the bonding length of the lattice and the Na–F coordinated interactions. This study provides more insights into the ionic doped crystal phase formation in such simple alkali metal fluorides, which may be of greater significance in the application as optical devices.
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