Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica

A unified moment tensor potential for silicon, oxygen, and silica

Si and its oxides have been extensively explored in theoretical research due to their technological importance. Simultaneously describing interatomic interactions within both Si and SiO2 without the use of ab initio methods is considered challenging, given the charge transfers involved. Herein, this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/SiO2/O system, based on the moment tensor potential (MTP) framework. This MTP is trained using a comprehensive database generated using density functional theory simulations, encompassing diverse crystal structures, point defects, extended defects, and disordered structure. Extensive testing of the MTP is performed, indicating it can describe static and dynamic features of very diverse Si, O, and SiO2 atomic structures with a degree of fidelity approaching that of DFT.

High-Temperature Atomic Diffusion and Specific Heat in Quasicrystals

A quasicrystal is an ordered but nonperiodic structure understood as a projection from a higher-dimensional periodic structure. Some physical properties of quasicrystals are different from those of conventional solids. An anomalous increase in heat capacity at high temperatures has been discussed for over two decades as a manifestation of a hidden high dimensionality of quasicrystals. A plausible candidate for this origin has been the phason, which has excitation modes originating from the additional atomic rearrangements introduced by the quasiperiodic order, which can be understood in terms of shifting a higher-dimensional lattice. However, most theoretical studies of phasons have used toy models. A theoretical study of the heat capacity of realistic quasicrystals or their approximants has yet to be conducted because of the huge computational complexity. To bridge this gap between experiment and theory, we show experiments and molecular simulations on the same material, an Al-Pd-Ru quasicrystal, and its approximants. We show that at high temperatures, aluminum atoms diffuse with discontinuouslike jumps, and the diffusion paths of the aluminum can be understood in terms of jumps corresponding to hyperatomic-fluctuations-associated atomic rearrangements of the phason degrees of freedom. It is concluded that the anomaly in the heat capacity of quasicrystals arises from the hyperatomic fluctuations that play a role in diffusive Nambu-Goldstone modes.

Synthesis and Characterization of the New Li1+xAl1+xSi1-xO4 (x = 0-0.25) Solid Electrolyte for Lithium-Ion Batteries

A systematic study was performed to investigate the effect of the sintering temperature, sintering duration, and aluminum doping on the crystalline structure and ionic conductivity of the Li1+xAl1+xSi1-xO4 (LASO; x = 0-0.25) solid electrolyte. There was a strong indication that an increase in the sintering temperature and sintering time increased the ionic conductivity of the electrolyte. In particular, the doping concentration and composition ratio (Li1+xAl1+xSi1-xO4; x = 0-0.25) were found to be crucial factors for achieving high ionic conductivity. The sintering time of 18 h and lithium concentration influenced the lattice parameters of the LASO electrolyte, resulting in a significant improvement in ionic conductivity from 2.11 × 10-6 (for pristine LASO) to 1.07 × 10-5 S cm-1. An increase in the lithium concentration affected the stoichiometry, and it facilitated a smoother Li-ion transfer process since lithium served as an ion-conducting bridge between LASO grains.

Machine learning molecular dynamics reveals the structural origin of the first sharp diffraction peak in high-density silica glasses

The first sharp diffraction peak (FSDP) in the total structure factor has long been regarded as a characteristic feature of medium-range order (MRO) in amorphous materials with a polyhedron network, and its underlying structural origin is a subject of ongoing debate. In this study, we utilized machine learning molecular dynamics (MLMD) simulations to explore the origin of FSDP in two typical high-density silica glasses: silica glass under pressure and permanently densified glass. Our MLMD simulations accurately reproduce the structural properties of high-density silica glasses observed in experiments, including changes in the FSDP intensity depending on the compression temperature. By analyzing the simulated silica glass structures, we uncover the structural origin responsible for the changes in the MRO at high density in terms of the periodicity between the ring centers and the shape of the rings. The reduction or enhancement of MRO in the high-density silica glasses can be attributed to how the rings deform under compression.

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.