arXiv subjects

Yoshihiro Kangawa

Publications and source records attributed to Yoshihiro Kangawa.

10 recordsLinked to original sources

arXiv2026

Influence of chemical vapor deposition conditions on N incorporation ratio on vicinal 4H-SiC(000-1) surface: Ab Initio-based approach

In experimental studies, increasing the ratio of source gases (C3H8/SiH4; C/Si ratio) for chemical vapor deposition (CVD) causes the growth rate to increase monotonically, and nitrogen incorporation tends to decrease accordingly; however, a unique phenomenon has been observed in which nitrogen incorporation increases discontinuously in the range of C/Si ratio between 1.0 and 1.5. This phenomenon is specific to the vicinal 4H-SiC(000-1) C-face and is not observed on the vicinal 4H-SiC(0001) Si-face. In this study, N incorporation behavior on a vicinal C-face during CVD is investigated using an ab initio-based approach. The calculation results suggest that when C/Si ratio is less than 1.0, the Si-terminated step edge is stable, whereas when C/Si ratio exceeds 1.0, the existence probability of the C-H-terminated step edge, where the N substitution energy is lower than in the former case, increases sharply. This change in the step-edge state across C/Si = 1.0 is thought to be the cause of the discontinuous increase in N incorporation.

cond-mat.mtrl-sci↗

arXiv2026

Weisfeiler-Lehman subtree encoding for Bayesian optimization of atomic configurations

The efficiency of Bayesian optimization (BO) of atomic configurations depends strongly on how configurations are encoded. We introduce the Weisfeiler-Lehman (WL) subtree kernel, which views configurations as element-labeled graphs and measures their similarity by how many local structural patterns they share, into Bayesian-optimization-based configuration search. Because this kernel is reproduced as the plain inner product of explicit features (L$^2$-normalized histograms of local topological patterns), introducing it reduces to introducing the corresponding features: the encoding enters existing BO frameworks as an ordinary descriptor. In a benchmark ground-state configuration search of cubic BC$_2$N evaluated with a universal machine-learning interatomic potential, the WL encoding reached the ground state almost immediately after a shared random initialization of 100 samples in every one of five independent rounds (108$\pm$5 evaluations on average), whereas the one-hot baseline required 280$\pm$122 evaluations; the WL-driven sampler first exhausted the degenerate ground-state group and then discovered the metastable degenerate groups from the bottom up, in order of increasing energy.

cond-mat.mtrl-sci↗

arXiv2026

Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001)

Atomic-scale understanding of the surface elementary processes in metalorganic vapor phase epitaxy (MOVPE) of GaN has so far relied on static density-functional-theory (DFT) energetics and on first-principles molecular dynamics (FPMD) limited to a few tens of picoseconds. Here we combine FPMD with a universal machine-learning interatomic potential (MLIP), UMA, to follow the dynamics of growth-related adspecies on GaN(0001) over time scales inaccessible to purely first-principles approaches. FPMD simulations of a GaNH admolecule coexisting with H adatoms reveal a hitherto unrecognized diffusion mode, in which the N atom lifts the Ga atom of the GaNH unit off the surface layer during migration, and show that the lifted Ga abstracts an H adatom from the surface, events invisible to static DFT. Single-point UMA calculations on FPMD snapshots reproduce the first-principles relative energies along the trajectory (RMSE of about 8.5 meV/atom) without any retraining. Long-time MLIP-based MD (150 ps) then reveals dynamics never observed within the FPMD window: site-to-site H-adatom hopping, which gates the migration paths of the growth unit, and reversible dissociation of the GaNH unit into independently migrating Ga and NH adspecies. This work constitutes, to our knowledge, the first application of an MLIP to the molecular dynamics of GaN MOVPE.

cond-mat.mtrl-sci↗

arXiv2026

Intrinsic Step Jamming in Nanometer-Scale KPZ-like Rough Surfaces under Interface-Limited Crystal Growth and Retreat

We investigate an intrinsic step-jamming phenomenon at the nanometer scale on Kardar-Parisi-Zhang (KPZ)-like kinetically roughened crystal surfaces that arises during interface-limited steady crystal growth or retreat. Monte Carlo simulations using the Metropolis algorithm on a restricted solid-on-solid (RSOS) lattice model demonstrate that intrinsic step jamming persists on surfaces below 20 nm. In the present model, transport processes such as surface and volume diffusion are excluded, as are elastic interactions, step-step repulsion or attraction, and stoichiometric effects. We show that intrinsic step jamming arises from asymmetric fluctuations in atomic attachment and detachment driven by biased transition probabilities under the SOS restriction, leading to collective step congestion. Asymmetric fluctuations also determine whether adatom or hole clusters grow or recede. This mechanism bears close similarity to jamming phenomena in the asymmetric simple exclusion process (ASEP), including multi-lane variants. In contrast, symmetric thermal fluctuations generate adatom or hole clusters on terraces, thereby suppressing intrinsic step jamming. Possible routes to suppress intrinsic step jamming, including experimentally accessible strategies, are also discussed.

cond-mat.mes-hall↗

arXiv2026

PyAPX: Python toolkit for atomic configuration pattern exploration

In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical composition, and elemental substitution, which explores chemical compositions that yield desirable properties in a given crystal structure. However, even when both the crystal structure and chemical composition are fixed, material properties can still vary depending on the atomic arrangements (configurations) at crystallographic sites. To support detailed material design, we present PyAPX, a Python toolkit that performs Bayesian searches of stable atomic configurations. A distinctive feature of this initial release is the introduction of encoding methods suitable for configuration search, and we evaluate their performance using the h-BCN system. As a result, they were confirmed to yield superior convergence compared to commonly used one-hot encoding. PyAPX is broadly applicable to crystalline materials and is expected to further advance materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Exploration of stable atomic configurations in graphene-like BCN systems by Bayesian optimization

h-BCN is an intriguing material system where the bandgap varies considerably depending on the atomic configuration, even at a fixed composition. Exploring stable atomic configurations in this system is crucial for discussing the energetic formability and controllability of desirable configurations. In this study, this challenge is tackled by combining first-principles calculations with Bayesian optimization. An encoding method that represents the configurations as vectors, while incorporating information about the local atomic environments and domain knowledge, is proposed for the search. The proposed encoding method proved effective in the search, resulting in the discovery of two interesting and stable semiconductor configurations. Furthermore, the optimization behavior is discussed through principal component analysis, confirming that the ordered BN network and the C configuration features are well embedded in the search space. While our approach provided a tailored encoding for the h-BCN system in this study, it holds promise for broader application to other materials by adapting the domain knowledge matrix to each target system.

cond-mat.mtrl-sci↗

arXiv2022

Beyond ab initio reaction simulator: an application to GaN metalorganic vapor phase epitaxy

To develop a quantitative reaction simulator, data assimilation was performed using high-resolution time-of-flight mass spectrometry (TOF-MS) data applied to GaN metalorganic vapor phase epitaxy system. Incorporating ab initio knowledge into the optimization successfully reproduces not only the concentration of CH$_4$ (an impurity precursor) as an objective variable but also known reaction pathways. The simulation results show significant production of GaH$_3$, a precursor of GaN, which has been difficult to detect in TOF-MS experiments. Our proposed approach is expected to be applicable to other applied physics fields that require quantitative prediction that goes beyond ab initio reaction rates.

cond-mat.mtrl-sci↗

arXiv2022

Polarization doping ab initio verification of the concept charge conservation and nonlocality

In this work, we study the emergence of polarization doping in AlxGa1-xN layers with graded composition from a theoretical viewpoint. We demonstrate that the charge conservation law applies for fixed and mobile charges separately, leading to nonlocal compensation phenomena involving bulk fixed and mobile charge and polarization sheet charge at the heterointerfaces. The magnitude of the effect allows obtaining technically viable mobile charge density for optoelectronic devices without impurity doping (donors or acceptors). Therefore, it provides an additional tool for the device designer, with the potential to attain high conductivities: high carrier concentrations can be obtained even in materials with high dopant ionization energies, and the mobility is not limited by scattering at ionized impurities.

cond-mat.mtrl-sci↗

arXiv2021

Exploration of a new reconstructed structure on GaN(0001) surface by Bayesian optimization

GaN(0001) surfaces with Ga- and H-adsorbates are fundamental stages for epitaxial growth of semiconductor thin films. We explore stable surface structures with nanometer scale by the density-functional calculations combined with Bayesian optimization, and succeed to reach a single structure with satisfactorily low mixing enthalpy among hundreds of thousand possible candidate structures. We find that the obtained structure is free from any postulated high symmetry previously introduced by human intuition, satisfies electron counting rule locally, and shows new adsorbate arrangement, reflecting characteristics of nitride semiconductors.

cond-mat.mtrl-sci↗

arXiv2020

Screw dislocation that converts p-type GaN to n-type: Microscopic study on the Mg condensation and the leakage current in p-n diodes

Recent experiments suggest that Mg condensation at threading dislocations induce current leakage, leading to degradation of GaN-based power devices. To study this issue, we perform first-principles total-energy electronic-structure calculations for various Mg and dislocation complexes. We find that threading screw dislocations (TSDs) indeed attract Mg impurities, and that the electronic levels in the energy gap induced by the dislocations are elevated towards the conduction band as the Mg impurity approaches the dislocation line, indicating that the Mg-TSD complex is a donor. The formation of the Mg-TSD complex is unequivocally evidenced by our atom probe tomography in which Mg condensation and diffusion through [0001] screw dislocations is observed in p-n diodes. These findings provide a novel picture that the Mg being a p-type impurity in GaN diffuses toward the TSD and then locally forms an n-type region. The appearance of this region along the TSD results the reverse leakage current.

cond-mat.mtrl-sci↗