arXiv subjects

Kazuma Ito

Publications and source records attributed to Kazuma Ito.

4 recordsLinked to original sources

arXiv2026

A nuclear-quantum-corrected machine-learning potential reveals quantum-enhanced hydrogen segregation at general grain boundaries in alpha-iron

Atomistic descriptions of hydrogen diffusion and trapping at defects are essential for understanding hydrogen embrittlement. As the lightest solute in metals, hydrogen exhibits nuclear quantum effects that alter these processes even at room temperature. Explicit treatment of such effects is computationally demanding, limiting large-scale simulations of complex environments. Here, we use an Fe-H machine-learning interatomic potential (MLIP) based on the performant implementation of the atomic cluster expansion (PACE), covering diverse Fe-H environments, and relabel the training configurations underpinning its transferability with quantum mean forces from centroid-constrained path-integral molecular dynamics at 300 K. This yields a nuclear-quantum-corrected PACE (NQC-PACE) without additional density functional theory calculations. At parent PACE, NQC-PACE describes nuclear quantum effects on hydrogen trapping at vacancies, dislocations, surfaces and general grain boundaries, H-H interactions, and diffusion in alpha-Fe. Grand-canonical Monte Carlo/molecular dynamics simulations show nuclear quantum effects markedly enhance hydrogen segregation at general grain boundaries and trapping behaviour in closer agreement with experimental trends. This enhancement arises from selective quantum stabilisation of open, anisotropically soft local environments. Our framework uses finite-temperature quantum mean forces to relabel the configurational space covered by an MLIP, enabling large-scale analysis of complex materials where light-element quantum effects matter.

cond-mat.mtrl-sci↗

arXiv2026

Machine-learned atomistic simulations reveal the basis of hydrogen-induced crack-plane transition in alpha-Fe

Hydrogen-related fracture in body-centered cubic Fe and ferritic steels often appears as transgranular quasi-cleavage rather than purely intergranular failure, especially at low to moderate hydrogen contents. Fractography has suggested that hydrogen may change the dominant cleavage faceting from {100} toward {110}, but atomic-scale evidence for this possible crack-plane transition remains unclear. Here we construct an efficient neural-network potential for {\alpha}-Fe/H and combine large-scale, three-dimensional molecular dynamics with grand-canonical Monte Carlo (GCMC), allowing the near-tip crack-surface region and crack tip within a defined GCMC domain to exchange hydrogen with a reservoir at fixed chemical potential. A comparison of four crack systems identifies the controlling response: (100)[010], (100)[011], and (110)[001] remain cleavage-dominated, whereas the (110)[1-10] crack changes from dislocation emission in pure Fe to cleavage under hydrogen charging. The energetic origin is twofold. Hydrogen lowers the Griffith cleavage threshold of the {110} cleavage-plane family more strongly than that of {100}, and, for the controlling crack, a Rice-type energetic descriptor indicates that the surface-energy-controlled cleavage resistance decreases faster than the unstable-stacking-fault-controlled emission resistance, consistent with a weakened dislocation-emission shield. These results provide a thermodynamically consistent atomistic basis for a hydrogen-induced transgranular crack-plane transition in Fe.

physics.comp-ph↗

arXiv2025

Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms

Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. However, the substantial computational expense associated with existing MLIPs has limited their applicability in practical, large-scale simulations. In this study, we construct a new MLIP within the Performant Implementation of the Atomic Cluster Expansion (PACE) framework, trained on a comprehensive HE-related dataset generated through a concurrent-learning strategy. The resulting potential achieves density functional theory-level accuracy in reproducing a wide range of lattice defects in alpha-Fe and their interactions with hydrogen, including both screw and edge dislocations. More importantly, it accurately captures the deformation and fracture behavior of nanopolycrystals containing hydrogen-segregated general grain boundaries-phenomena not explicitly represented in the training data. Despite its high fidelity, the developed potential requires computational resources only several tens of times greater than empirical potentials and is more than an order of magnitude faster than previously reported MLIPs. By delivering both a high-precision and computationally efficient potential, as well as a generalizable methodology for constructing such models, this study significantly advances the atomic-scale understanding of HE across a broad range of metallic materials.

cond-mat.mtrl-sci↗

arXiv2024

Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation

Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is challenging due to the high cost of electronic structure calculations which would be needed to compute the structures and properties of interest such as the material's stability and ion diffusion properties. The soft bond valence (SoftBV) approach is attractive for rapid prescreening among multiple compositions and structures, but the simplicity of the approximation can make the results inaccurate. We explore the possibility of enhancing the accuracy of the SoftBV approach when estimating crystal structures by adapting the parameters of the approximation to the chemical composition. Specifically, on the examples of perovskite- and spinel-type oxides that have been proposed as promising solid-state ionic conductors, the screening factor, an independent parameter of the SoftBV approximation, is modeled using linear and non-linear methods as a function of descriptors of chemical composition. We find that making the screening factor a function of composition can noticeably improve the ability of SoftBV to correctly model structures, in particular new, putative crystal structures whose structural parameters are yet unknown. We also analyze the relative importance of nonlinearity and coupling in improving the model and find that while the quality of the model is improved by including nonlinearity, coupling is relatively unimportant. While using a neural network showed no improvement over linear regression, the recently proposed GPR-NN method that is a hybrid between a single hidden layer neural network and kernel regression showed substantial improvement, enabling the prediction of structural parameters of new ceramics with accuracy on the order of 1%.

cond-mat.mtrl-sci↗