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.

Atomistic weak interaction criterion for the specificity of liquid metal embrittlement.

Liquid metal embrittlement (LME) occurs in some solid-liquid metal elements' couples (e.g., Fe-Zn and Al-Ga), called specificity. Although some material parameters like solubility and bonding energy were suggested as controlling factors, none could be attributed satisfactorily. Here we have unveiled the primary factor that governs the specificity of LME. From first-principles calculations compared with a systematic surveillance test result, we found that the grain-boundary (GB) adsorption energy shows near-zero values in all embrittling couples; the interaction between solid and liquid metal atoms is weak when an atom from the liquid state penetrates the grain boundary of the solid. Furthermore, we found that the calculated surface adsorption energy that promotes bond-breaking does not correlate to the specificity. Therefore, we consider that the penetration of a liquid metal atom surrounded by weakly interacting solid metal atoms is necessary before the bond-breaking assisted by surface adsorption occurs at a microcrack tip. This mechanism is also applicable for transgranular cracking along low-energy boundaries and crystal planes. While liquid metal atoms penetrate and diffuse into solid GB macroscopically before cracking, liquid metal's surface adsorption stronger than GB adsorption should promote the bond-breaking of solid metal. In conclusion, the atomistic penetration precedes the surface-adsorption-assisted bond-breaking and controls the specificity of LME.

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 [Formula: see text] 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.

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

Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report [Nagai et al., Phys. Rev. B 102, 041124(R) (2020)], the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy x-ray data.

Hydrogen-accelerated spontaneous microcracking in high-strength aluminium alloys.

Aluminium alloys are re-evaluated as most feasible way to satisfy the industrial needs of light-weight structural materials. However, unlike conventional structural metals such as iron and titanium, aluminium does not have easily accessible secondary phases, which means that aluminium-based alloys cannot be strengthened by harnessing multiple phases. This leaves age hardening as the only feasible strengthening approach. Highly concentrated precipitates generated by age hardening generally play a dominant role in shaping the mechanical properties of aluminium alloys. In such precipitates, it is commonly believed that the coherent interface between the matrix and precipitate does not contribute to crack initiation and embrittlement. Here, we show that this is not the case. We report an unexpected spontaneous fracture process associated with hydrogen embrittlement. The origin of this quasi-cleavage fracture involves hydrogen partitioning, which we comprehensively investigate through experiment, theory and first-principles calculations. Despite completely coherent interface, we show that the aluminium-precipitate interface is a more preferable trap site than void, dislocation and grain boundary. The cohesivity of the interface deteriorates significantly with increasing occupancy, while hydrogen atoms are stably trapped up to an extremely high occupancy over the possible trap site. Our insights indicate that controlling the hydrogen distribution plays a key role to design further high-strength and high-toughness aluminium alloys.

Novel Cross-Slip Mechanism of Pyramidal Screw Dislocations in Magnesium.

Compared to cubic metals, whose primary slip mode includes twelve equivalent systems, the lower crystalline symmetry of hexagonal close-packed metals results in a reduced number of equivalent primary slips and anisotropy in plasticity, leading to brittleness at the ambient temperature. At higher temperatures, the ductility of hexagonal close-packed metals improves owing to the activation of secondary ⟨c+a⟩ pyramidal slip systems. Thus, understanding the fundamental properties of corresponding dislocations is essential for the improvement of ductility at the ambient temperature. Here, we present the results of large-scale ab initio calculations for ⟨c+a⟩ pyramidal screw dislocations in magnesium and show that their slip behavior is a stark counterexample to the conventional wisdom that a slip plane is determined by the stacking fault plane of dislocations. A stacking fault between dissociated partial dislocations can assume a nonplanar shape with a negligible energy cost and can migrate normal to its plane by a local shuffling of atoms. Partial dislocations dissociated on a {21[over ¯]1[over ¯]2} plane "slither" in the {011[over ¯]1} plane, dragging the stacking fault with them in response to an applied shear stress. This finding resolves the apparent discrepancy that both {21[over ¯]1[over ¯]2} and {011[over ¯]1} slip traces are observed in experiments while ab initio calculations indicate that dislocations preferably dissociate in the {21[over ¯]1[over ¯]2} planes.