To elucidate the mechanism of hydrogen embrittlement in steel, quantifying the interaction between hydrogen and lattice defects is essential. However, the hydrogen trapping energy at edge dislocation cores in ferromagnetic body-centered cubic (bcc) iron remains elusive, as the long-range elastic strain field surrounding the cores complicates accurate estimation via first-principles density functional theory (DFT) calculations with small computational cells. In this study, we overcame this limitation by combining DFT with a machine-learning neural network potential (NNP). First, we calculated the hydrogen trapping energy using DFT in a periodic boundary cell containing two oppositely signed dislocation cores, with a cell size of up to approximately 400 atoms. After verifying that the NNP can reasonably reproduce the DFT results, we performed NNP calculations with enlarged cell sizes to quantify the long-range elastic strain interactions between dislocation cores. Subtracting this elastic contribution from the DFT results successfully removed finite-cell-size effects. In addition to the a0/2[-1-11](-101) (a0: lattice constant) edge dislocation core addressed in our previous work, this study expanded the scope of analysis to a broader range of dislocation core structures: the a0/2[111](-101) 71° mixed, the a0/2[111](-1-12) edge, the a0[100](011) and the a0[100](010) binary-junction dislocation cores, as well as a single vacancy. The calculated trapping energies for specific dislocation cores and a single vacancy exhibit excellent agreement with the hydrogen desorption activation energies of the deepest trapping sites reported in recent low-temperature thermal desorption spectrometry on a heavily deformed pure iron subjected to 25% plastic strain.

Accurate impurity thermodynamics in liquid metals is essential for modeling corrosion, mass transport, and safety-relevant chemistry, but direct first-principles sampling remains computationally costly, and GGA-based treatments often require empirical corrections. Here, we combine SCAN-family meta-GGA thermochemistry with machine-learning molecular dynamics to evaluate the oxygen solution enthalpy in liquid sodium. Moment tensor potentials trained on SCAN, rSCAN, and r2SCAN reference data enable long liquid state sampling with near-first-principles fidelity. Across the SCAN family, the computed O2-referenced solution enthalpies agree with experimentally inferred values without empirical O2 corrections. This indicates that the dominant error in the corresponding PBE-based treatment originates from the gas phase reference rather than from the liquid state description. The calculations also predict a stronger temperature dependence than is implied by a single Arrhenius slope fitted to solubility data. Together, these results establish SCAN-family thermochemistry combined with machine-learning molecular dynamics as an accurate and scalable framework for quantitative impurity thermodynamics in liquid metals.

We successfully reproduced the experimental thermal desorption spectra of hydrogen from a small iron sample containing hydrogen-enhanced strain-induced vacancies by revising a previously proposed numerical model (Ebihara et al. in Metall Mater Trans A 52A:257, 2021). In the revised model, we adopted concentration variables for mono- and cluster-vacancies, which are separately defined based on the number of trapped hydrogen atoms. This eliminates the continuous-number representation of the number of trapped hydrogen evaluated from the product of the hydrogen occupancy and the trap site concentration in the original model. According to the revision, the migration of mono- and cluster-vacancies trapping hydrogen, which must be assumed to simulate the thermal desorption spectra in the original model, became unnecessary. Simulation results obtained using the revised model revealed that the spike-like desorption on the peak attributed to mono- and cluster-vacancies in the spectra simulated by the original model was an artifact caused by the migration of mono- and cluster-vacancies trapping hydrogen. Furthermore, it was also suggested that not only mono-vacancies but also cluster-vacancies can exist in the specimens after deformation during hydrogen charging. Additionally, it was confirmed that dissociation of a mono-vacancy from cluster-vacancies trapping of hydrogen atoms needs to be considered to examine the effect of the thermal aging process, in which both hydrogen atoms and vacancies are decreased.

Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at low temperatures, where spin–orbit coupling produces strong coupling between spin and lattice degrees of freedom. Direct DFT simulations of magnetic phase transitions at finite temperatures are computationally prohibitive. Here, we develop a spin neural network potential (SpinNNP) that explicitly incorporates spin degrees of freedom and is trained on DFT+U reference data including spin–orbit coupling effects. The SpinNNP accurately reproduces DFT energies, atomic forces, spin precession vectors, and lattice constants. Although the predicted magnetic ground state differs from experiment due to known limitations of the underlying DFT description, machine-learning molecular dynamics simulations with spin dynamics successfully reproduce the antiferromagnetic–paramagnetic transition and its associated thermodynamic signatures. These results demonstrate that machine-learning potentials can enable large-scale spin–lattice simulations of actinide oxides and provide a practical route toward predictive modeling of complex magnetic materials.

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine learning molecular dynamics (MLMD) simulations to investigate the high-temperature thermal properties of thorium dioxide, which has a fluorite structure, and lithium oxide, which has an anti-fluorite structure. Our results show that MLMD simulations effectively reproduce the reported thermal properties of these materials. A central focus of this work is the analysis of specific heat anomalies in these materials at high temperatures, commonly referred to as Bredig, pre-melting, or λ-transitions. We demonstrate that a local order parameter, analogous to those used to describe liquid-liquid transitions in supercooled water and liquid silica, can effectively characterize these specific heat anomalies. The local order parameter identifies two distinct types of defective structures: lattice defect-like and liquid-like local structures. Above the transition temperature, liquid-like local structures predominate and the sub-lattice character of mobile atoms disappears.

Hydrogen-enhanced decohesion (HEDE) is a proposed mechanism of hydrogen-induced grain boundary (GB) fracture in metals and has been widely calculated from first principles over the past decade. However, the effect of GB-segregated solutes on HEDE is complex and rarely quantified. This study presents a quantitative numerical estimation method based on statistical thermodynamics using first-principles calculations of multiple hydrogen trappings at a GB and its fracture surfaces with segregated solutes. This method accurately estimates the lattice-dissolution-hydrogen-dependent HEDE, including the interactions caused by the segregated solute: the decohering or cohesion-enhancing effect of the solute itself, solute-hydrogen interaction, solute-affected hydrogen-hydrogen interaction, and mobile hydrogen effect. We present a trial calculation to examine how the attractive interaction between solute and hydrogen influences HEDE, showing that HEDE can be induced at lower hydrogen concentrations if not canceled by other interactions.

Understanding the mechanism of hydrogen embrittlement in steel requires knowledge of hydrogen trapping behavior at lattice defects in iron. However, first-principles calculations using atomistic modeling of an edge dislocation core in body-centered cubic ferromagnetic iron remain challenging because they require several hundred atoms for the core structure and must account for the influence of a long-range strain field around the core. We calculated the hydrogen trapping energies at iron's most common edge dislocation core from first principles; we used a relatively small unit cell (378 Fe atoms) containing two cores of opposite signs with periodic boundary conditions. The cell size dependence of the hydrogen trapping energies was estimated using a recently developed machine-learning neural network potential for the iron-hydrogen system. Although the small cell size led to overestimating the trapping energy, it was less than 10 %.

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.

Dilute Mg-Zn-Y alloy with a mille-feuille structure (MFS) exhibits a mechanical strength comparable to Mg-Zn-Y alloy with long period stacking/ordered (LPSO) structure through kink deformation. In order to deepen understanding the thermal stability of the MFS-type Mg alloys, it is required to clarify the solute cluster structures composed of Zn and Y in solute enriched stacking faults (SESFs). In this study, electron energy-loss and energy dispersive X-ray spectroscopy based on scanning transmission electron microscopy (STEM-EELS/EDS) were conducted to investigate the electronic structure and composition of Zn and Y in the SESFs of the MFS-Mg alloy. Zn-L2,3 spectra indicated that the valence charges of Zn in the dilute Mg alloy were different from that of the LPSO-type Mg-Zn-Y alloy. In addition, the intensity ratio of L3/L2 in Y-L2,3 spectrum of the dilute MFS-Mg alloy was larger than that of the LPSO-Mg alloy, reflecting the electron occupancies of 4d3/2 and 4d5/2 orbitals of Y atoms were different from those of the LPSO-Mg alloys. STEM-EELS analysis of the SESF composition in the dilute MFS-Mg alloy indicated that the Zn/Y ratio should be lower than that of the LPSO-Mg alloy, which was confirmed also by STEM-EDS measurements. These results indicate that the cluster structure in the SESFs of the dilute MFS-Mg alloy should be different from the ideal Zn6Y8 cluster in the LPSO-type Mg-Zn-Y alloys.

On-the-fly kinetic Monte Carlo (kMC), a computational technique for atomistic simulations, has attracted attention because it increases the simulation timescale beyond that of molecular dynamics (MD) simulations while maintaining atomistic fidelity. However, for most kMC methods, when events with high and low activation energies coexist in the event list, trivial events with extremely low activation energies that do not essentially affect the phenomena of interest, so-called flicker events, are frequently selected, making it challenging to observe the key dynamics. In this study, we use Self-Evolving Atomistic kMC (SEAKMC), one of the on-the-fly kMC methods, to model the unstable-to-stable transformations of irregular three-dimensional self-interstitial-atom (SIA) clusters in Cu generated through collision cascade. By setting an activation energy threshold once every five steps, transformations into stable configurations are enhanced. The algorithm renders the simulation timescales one or two orders of magnitude longer than those possible with MD simulations. Further, the probability of transformations into stable configurations is increased by 40 times compared to that of the original SEAKMC method. In addition, we find that the stable configurations obtained by the transformation of the SIA clusters are mostly Frank loops. In summary, this new algorithm for the SEAKMC method helps to resolve the inefficiency of kMC methods resulting from the selection of flicker events and will aid the study of meso-timescale atomistic dynamics.

We attempted to calculate the hydrogen trapping energies at the incoherent interfaces of MgZn2 precipitates and Mg2Si crystallites in aluminum alloys from first-principles calculations. Since the unit cell containing the incoherent interface does not satisfy the periodic boundary condition, resulting in a discontinuity of crystal blocks, the hydrogen trapping energy was calculated in a region far from the discontinuity (vacuum) region. We found considerable trapping energies for hydrogen atoms at the incoherent interfaces consisting of assumed atom istic arrangement. W e also conducted preliminary calculations of the reduction i n the cohesive energy by hydrogen trapping on the incoherent interfaces of Mg2Si in the aluminum matrix.

Kink boundaries formed in Mg-based long period stacking order (LPSO) alloys play a key role in strengthening of these materials. As the kink structure grows, many high-angle kink boundaries are eventually formed which has inclination angle close to 34 degrees. We show that this peculiar structure is a result of irreversible structural transformation and is energetically stable. We also calculate segregation energies of alloying elements Y and Zn to this boundary. Finally, the critical resolved shear stress for the migration of kink boundary is estimated for a pure-Mg kink and that with saturated with segregation. We show that segregated kink boundary requires very high shear stress about 700 MPa for migration.

The microstructure and mechanical properties of Al-Zn-Mg alloys with low Zn/Mg ratios have been studied.According to various researchers, the major strengthening is due to η-phase and T phase. There are many briefly research on the microstructure and interface of the η-phase types but not briefly information about the T-phase That’s why now our aim to work on T-phase. In actual our aim is to observe the T-phase interface. The MgZn2 phase (η phase) and its metastable phase (η′ phase) were the most prominent precipitates. Another study revealed various Mg32(Al,Zn)49 phases (T phase) and their metastable phase (T′ phase) in Al-Zn-Mg alloys with low Zn and high Mg content. Al-Zn-Mg alloys with a Zn/Mg ratio of 0.71 were explored for this study. The alloy with a Zn/Mg ratio of 0.71 aged at 473k for 2000 minutes exhibited the highest hardness, according to the observations. The strengthening precipitates in the investigated alloy were totally T′ phase, according to TEM observation.

He bubbles are characteristic microstructures under fusion reactor conditions. They approach and coalesce through their own migration, which significantly impacts the microstructure and material properties. However, these processes, which involve multiple migrations of metal atoms, cannot be treated by molecular dynamics (MD) due to its timescale limitation. In this study, self-evolving atomistic kinetic Monte Carlo (SEAKMC) was used to expand the timescale and reproduce bubble coalescences in Fe. To enhance selections of events that led to the process by avoiding trivial events with an extremely low activation energy such as tiny vibrations of a He atom or short-range displacements of the Fe atom, we introduced two algorithms into SEAKMC, a two-step saddle point search for the former measure and setting a threshold for a displacement distance of the Fe atom for the latter. Furthermore, by adding another algorithm to set an upper bound for the activation energy to prevent selections of events with an impractically high activation energy, we succeeded to reproduce the change in the configuration from dumbbell to elliptical up to a simulated time of 10-1 s, 8 orders longer than MD timescales. The developed method is effective for analyzing microstructures of metallic materials containing light elements and is the only method that can reach timescales comparable to those of experiments.

The introduction of obstacles (e.g., precipitates) for controlling dislocation motion in molecular structures is a prevalent method for designing the mechanical strength of metals. Owing to the nanoscale size of the dislocation core (<= 1 nm), atomic modeling is required to investigate the interactions between the dislocation and obstacles. However, conventional empirical potentials are not adequately accurate in contrast to calculations based on density functional theory (DFT). Therefore, the atomic-level details of the interactions between the dislocations and obstacles remain unclarified. To this end, in this paper, we applied an artificial neural network (ANN) framework to construct an atomic potential by leveraging the high accuracy of DFT. Using the constructed ANN potential, we investigated the dynamic interaction between the (a(0)/2)< 111 >{110} edge dislocation and obstacles in body-centered cubic (bcc) iron. When the dislocation crossed the void, an ultrasmooth and symmetric half-loop was observed for the bowing-out dislocation. Except for the screw dislocation, the Peierls stress of all the dislocations predicted using the ANN was <100 MPa. More importantly, the results confirmed the formation of an Orowan loop in the interaction between a rigid sphere and dislocation. Furthermore, we discovered a phenomenon in which the Orowan loop disintegrated into two small loops during its interaction with the rigid sphere and dislocation.

We investigated the electronic structure of the Mg99.2Zn0.2Y0.6 alloy using hard and soft X-ray photoemission spectroscopy and electronic band structure calculations to understand the mechanism of the phase stability of this material. Electronic structure of the Mg99.2Zn0.2Y0.6 alloy showed a semi-metallic electronic structure with a pseudo-gap at the Fermi level. The observed electronic structure of the Mg99.2Zn0.2Y0.6 alloy suggests that the presence of a pseudogap structure is responsible for phase stability.

We have evaluated local elastic properties at kink boundaries in dilute M g-Zn-Y alloys based on scanning transmission electron microscopy (STEM), first-principles calculations, and geometrical phase analysis ( GPA) by measuring atomic-scale strain fields around dislocation cores. STEM observations showed that dislocations constituting kink boundaries are extended into Shockley partial dislocations by accompanying solute-enriched stacking faults (SESF). Using GPA analysis, we found that strain distributions around the partial dislocation cores are asymmetric across the Mg matrix and the SESF, showing local elastic heterogeneity. Comparison with the calculated strain field model, it is indicated that the observed asymmetric strain profiles are essentially caused by a softening of the Mg matrix. This anomalous elastic softening can be interpreted as a change in strain energy of dislocations at the kink boundary, which may contribute t o the unique deformation mechanism of kink i n dilute Mg alloys.

High-entropy alloys (HEAs) have received attention because of their excellent mechanical and thermodynamic properties. A recent study revealed that Co-free face-centered cubic HEAs could improve strength and ductility, which is crucial for nuclear materials. Here we implemented first-principles calculations to explore the fundamental mechanism for enhancing the mechanical properties in Co-free Cr25Fe25Ni25Mn25 alloy. We found that the local lattice distortion of Co-free HEA was more significant than that of the well-known Cantor alloy. Furthermore, the short-range order formation in Co-free HEA caused the highly fluctuated stacking fault energy. Thus, the significant local lattice distortion and the non-uniform solid solution states comprising low- and high-stacking fault regions improve strength and ductility.