arXiv subjects

Masato Kotsugi

Publications and source records attributed to Masato Kotsugi.

5 recordsLinked to original sources

arXiv2026

Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe-Ge-Te Composition-spread Thin Film Libraries

Recently, magnetic 2-dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ~ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature. To explore the expanded compositional space, we implemented combinatorial synthesis and high-throughput characterization to investigate the structural phase distribution and ferromagnetism of a Fe-Ge-Te thin film library. The library was prepared by magnetron co-sputtering followed by annealing in vacuum or in an inert environment. Composition and structural phase distribution of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray diffraction (XRD), and two-point probe resistance measurements. We leverage unsupervised machine learning to cluster the XRD dataset into groups of compositions with similar structural phases, and further study the ferromagnetic properties via SQUID magnetometry and X-ray magnetic circular dichroism (XMCD) across different clusters. The results are compared against magnetization and structural models calculated using DFT. Our results demonstrate that the hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system, and that unexplored materials adopting this structure can be efficiently identified as possible ferromagnetic materials using our high-throughput, ML-assisted framework. This workflow based on the combinatorial strategy allows us to rapidly capture the composition-structure-magnetic property map across a broad compositional landscape of novel magnetic materials.

cond-mat.mtrl-sci↗

arXiv2026

Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES

Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional theory (DFT) and machine learning (ML) to interpret how boron (B) and nitrogen (N) dopants influence the local electronic environments of graphene. A dataset of 415 DFT-simulated XANES spectra from 91 distinct configurations was analyzed using a region-specific approach by decomposing each spectrum into pi*, sigma*, and post-edge regions. Random forest models trained on these spectral segments identified the pi* region as the most informative for predicting key local electronic descriptors, particularly the Bader charge and mean dopant-carbon bond length. The Bader charge quantifies dopant-induced charge redistribution and local bonding polarity, directly reflecting the degree of electronic perturbation introduced by heteroatom substitution. The enhanced predictive power of the pi* region arises from its strong coupling to the perturbed pi-electron network, which captures these charge-transfer and hybridization effects more effectively than sigma* or post-edge regions. These findings establish Bader charge as a robust and physically meaningful descriptor for quantifying dopant-induced electronic modulation and demonstrate that region-resolved ML analysis of XANES spectra provides a powerful pathway to uncover structure-property relationships in doped graphene and related materials.

cond-mat.mtrl-sci↗

arXiv2026

Inverse-design of two-dimensional magnonic crystals via topology optimization with frequency-domain micromagnetics

Magnonic crystals (MCs) are emerging spintronic metamaterials capable of manipulating transmission properties of magnons, the quanta of spin waves. Due to the complex relationship between lattice geometry and magnonic band dispersion, it remains challenging to establish general design strategies for optimizing targeted properties in MCs. In this study, we demonstrated an inverse-design framework for two-dimensional MCs to explore unconventional lattice structures with large magnonic band gaps. We employed genetic algorithms to enable global exploration of structures with a complete band gap as the objective property, and used frequency-domain micromagnetic simulations for computationally efficient band gap evaluation. Our established inverse-design method successfully discovered several previously unreported designs of MCs, whose performance was validated using time-domain micromagnetic simulations. Furthermore, we observed that the design landscape becomes increasingly non-convex at high-order bands, suggesting the existence of multiple design solutions. The overall inverse-design framework is expected to be broadly applicable to experimentally accessible material systems and device dimensions, facilitating the formulation of design rules for MCs.

cond-mat.mtrl-sci↗

arXiv2025

Persistent Homology-Based Indicator of Orientational Ordering in Two-Dimensional Quasi-Particle Systems Applied to Skyrmion Lattices

Two-dimensional (2D) particle systems, such as magnetic skyrmions, exhibit topological phase transitions between unique 2D phases. However, a simple and computationally efficient methodology to capture lattice configurational properties and construct an appropriate, easily calculable descriptor for phase identification remains elusive. Here, we propose an indicator for topological phase transitions using persistent homology (PH). PH offers novel insights beyond conventional indicators by capturing topological features derived from the configurational properties of the lattice. The proposed persistent-homology-based indicator, which selectively counts stable features in a persistence diagram, effectively traces the lattice's ordering changes, as confirmed by comparisons with the conventionally used measure of the ordering (the magnitude of the orientational order parameter $\langle|\Psi_6|\rangle$), typically used to identify lattice phases. We demonstrate the applicability of our indicator to experimental data, showing that it yields results consistent with those of simulations. This experimental validation highlights the robustness of the proposed method for real physical systems beyond idealized simulated systems. While our method is demonstrated in the context of skyrmion lattice systems, the approach is general and can be extended to other two-dimensional systems composed of interacting particles. As a key advantage, our indicator offers lower computational complexity than the conventionally used measures.

cond-mat.stat-mech↗

arXiv2024

Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys

The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials. Keywords: L10, FePt, machine learning, ab initio calculations, Bayesian optimization

cond-mat.mtrl-sci↗