arXiv subjects

Hiori Kino

Publications and source records attributed to Hiori Kino.

At least 19 recordsLinked to original sources

arXiv2026

Crystal-structure design by agentic AI in a language of motifs

Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types. We present MatEvolve, an agentic-AI framework designing crystals, proposing each candidate with a stated rationale and testing it. The agent reasons in an interpretable \emph{language of motifs}, writing each crystal as a \emph{motif profile} that describes the recurring geometric patterns---the \emph{motifs}---composing it. The motif profile serves not merely as a description of a material but as the medium for material design: the agent edits the profile and constructs a crystal from the modified one, and the most promising candidates are validated by first-principles calculation. Applied to the design of rare-earth-lean permanent magnets, MatEvolve---built on the state-of-the-art language model Claude Fable~5 without fine-tuning---reaches new structural prototypes more than three times as often as generative models under an equal validation budget, at a comparable on-target-magnet rate. Beyond design, analysing the discovered crystals' human-readable profiles reveals structure--property relationships.

cond-mat.mtrl-sci↗

arXiv2025

Superconductor discovery in the emerging paradigm of Materials Informatics

The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., hundreds of GPa. These discoveries were heavily driven by Migdal-Éliashberg theory (and its first-principles computational implementations) for electron-phonon interactions, the key concept of phonon-mediated superconductivity. Dozens of predictions were experimentally synthesized and characterized, triggering not only enormous excitement in the community but also some debates. In this Article, we review the computational-driven discoveries and the recent developments in the field from various essential aspects, including the theoretical, computational, and, specifically, artificial intelligence (AI)/machine learning (ML) based approaches emerging within the paradigm of materials informatics. While challenges and critical gaps can be found in all of these approaches, AI/ML efforts specifically remain in its infant stage for good reasons. However, opportunities exist when these approaches can be further developed and integrated in concerted efforts, in which AI/ML approaches could play more important roles.

cond-mat.supr-con↗

arXiv2025

Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery

Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and complex phase formation mechanisms. Efficient exploration of this space requires a strategic approach that integrates heterogeneous knowledge sources. Here, we propose a framework that systematically combines knowledge extracted from computational material datasets with domain knowledge distilled from scientific literature using large language models (LLMs). A central feature of this approach is the explicit consideration of element substitutability, identifying chemically similar elements that can be interchanged to potentially stabilize desired HEAs. Dempster-Shafer theory, a mathematical framework for reasoning under uncertainty, is employed to model and combine substitutabilities based on aggregated evidence from multiple sources. The framework predicts the phase stability of candidate HEA compositions and is systematically evaluated on both quaternary alloy systems, demonstrating superior performance compared to baseline machine learning models and methods reliant on single-source evidence in cross-validation experiments. By leveraging multi-source knowledge, the framework retains robust predictive power even when key elements are absent from the training data, underscoring its potential for knowledge transfer and extrapolation. Furthermore, the enhanced interpretability of the methodology offers insights into the fundamental factors governing HEA formation. Overall, this work provides a promising strategy for accelerating HEA discovery by integrating computational and textual knowledge sources, enabling efficient exploration of vast compositional spaces with improved generalization and interpretability.

cs.LG↗

arXiv2022

Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics

As interdisciplinary science is flourishing because of materials informatics and additional factors; a systematic way is required for expressing knowledge and facilitating communication between scientists in various fields. A function decomposition tree is such a representation, but domain scientists face difficulty in constructing it. Thus, this study cites the general problems encountered by beginners in generating function decomposition trees and proposes a new function decomposition representation method based on a causality-first perspective for resolution of these problems. The causality-first decomposition tree was obtained from a workflow expressed according to the processing sequence. Moreover, we developed a program that performed automatic conversion using the features of the causality-first decomposition trees. The proposed method was applied to materials informatics to demonstrate the systematic representation of expert knowledge and its usefullness.

cs.AI↗

arXiv2020

Explainable Machine Learning for Materials Discovery: Predicting the Potentially Formable Nd-Fe-B Crystal Structures and Extracting Structure-Stability Relationship

New Nd-Fe-B crystal structures can be formed via the elemental substitution of LATX host structures, including lanthanides LA, transition metals T, and light elements X as B, C, N, and O. The 5967 samples of ternary LATX materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure stability relationship of the newly created NdFeB crystal structures. For predicting the stability for the newly created NdFeB structures, three supervised learning models, kernel ridge regression, logistic classification, and decision tree model, are learned from the LATX host crystal structures; the models achieve the maximum accuracy and recall scores of 70.4 and 68.7 percent, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieves accuracy and recall score of 72.9 and 82.1 percent, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure stability relationship of the NdFeB crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted NdFeB crystal structures.

cond-mat.mtrl-sci↗

arXiv2020

Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature

We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture model. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature (TC) of binary 3d transition metal 4f rare earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

stat.ML↗

arXiv2019

Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness

In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth transition metal crystalline compounds represented using the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We perform predictions of the formation energies using k-nearest neighbors regression, ridge regression, and kernel ridge regression. Through detailed analyses of the yield prediction accuracy, we examine the relationship between the characteristics of the material representation and similarity measures, and the complexity of the energy function they can capture. Empirical experiments and theoretical analysis reveal that similarity measures and kernels that minimize the loss of materials distinctiveness improve the prediction performance.

cs.LG↗

arXiv2018

Important descriptors and descriptor groups of Curie temperatures of rare-earth transition-metal binary alloys

We analyze Curie temperatures of rare-earth transition metal binary alloys with machine learning method. In order to select important descriptors and descriptor groups, we introduce newly developed subgroup relevance analysis and adopt the hierarchical clustering in the representation. We execute the exhaustive search and successfully illustrate the importance of descriptors and descriptor groups. We execute the exhaustive search and illustrate that our approach indeed leads to the successful selection of important descriptors and descriptor groups. It helps us to choose the combination of the descriptors and to understand the meaning of the selected combination of descriptors.

cond-mat.mtrl-sci↗

arXiv2018

Effect of $R$-site substitution and the pressure on stability of $R$Fe$_{12}$: A first-principles study

We theoretically study the structural stability of $R$Fe$_{12}$ with the ThMn$_{12}$ structure ($R$: rare-earth elements, La, Pr, Nd, Sm, Gd, Dy, Ho, Er, Tm, Lu, Y, or Sc, or group-IV elements, Zr or Hf) based on density functional theory. The formation energy has a strong correlation with the atomic radius of $R$. The formation energy relative to simple substances decreases as the atomic radius decreases, except for $R=$ Sc and Hf, while that relative to $R_{2}$Fe$_{17}$ and bcc Fe has a minimum for $R=$ Dy. The present results are consistent with recent experimental reports in which the partial substitution of Zr at $R$ sites stabilizes $R$Fe$_{12}$-type compounds with $R=$ Nd or Sm. Our results also suggest that the partial substitution of Y, Dy, Ho, Er, or Tm for Nd or Sm is a possible way to enhance the stability of the ThMn$_{12}$ structure. Under hydrostatic pressure, the formation enthalpy decreases up to $\approx$ 6 GPa and then starts to increase at higher pressures.

cond-mat.mtrl-sci↗

arXiv2017

A regression-based feature selection study of the Curie temperature of transition-metal rare-earth compounds: prediction and understanding

The Curie temperature ($T_C$) of binary alloy compounds consisting of 3$d$ transition-metal and 4$f$ rare-earth elements is analyzed by a machine learning technique. We first demonstrate that nonlinear regression can accurately reproduce $T_C$ of the compounds. The prediction accuracy for $T_C$ is maximized when five to ten descriptors are selected, with the rare-earth concentration being the most relevant. We then discuss an attempt to utilize a regression-based model selection technique to learn the relation between the descriptors and the actuation mechanism of the corresponding physical phenomenon, i.e., $T_C$ in the present case.

cond-mat.mtrl-sci↗

arXiv2017

Machine learning reveals orbital interaction in crystalline materials

We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Our experiment shows that the formation energies of crystalline materials, the atomization energies of molecular materials, and the local magnetic moments of the constituent atoms in transition metal--rare-earth metal bimetal alloys can be predicted with high accuracy using the OFM. Knowledge regarding the role of coordination numbers of transition-metal and rare-earth metal elements in determining the local magnetic moment of transition metal sites can be acquired directly from decision tree regression analyses using the OFM.

cond-mat.mtrl-sci↗

arXiv2017

Model-mapped RPA for determining the effective Coulomb interaction

We present a new method to obtain interaction part of a model Hamiltonian from the result of the first-principles calculation. The effective interaction contained in the model is determined based on the random phase approximation (RPA). In contrast to previous methods such as projected RPA or constrained RPA, the new method takes into account the long-range part of the polarization effect when determining the interaction in the model. After we discuss problems in previous RPA methods, we will give the formulation of the new method, and show how it works for the single-band Hubbard model of HgBa$_2$CuO$_4$.

cond-mat.mtrl-sci↗

arXiv2016

First-principles study on stability and magnetism of NdFe11M and NdFe11MN for M=Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn

Recently synthesized NdFe12N has excellent magnetic properties, while it is thermodynamically unstable. Using first-principles method, we study the effect of substitutional 3d transition metal elements to the mother compound NdFe12. We find that Co has positive effect on the stability of the ThMn12 structure. In contrast with Ti substitution, Co substitution does not reduce the magnetization significantly. The crystal field parameter A_{0}^{2} is nearly unchanged by Co substitution, and nitrogenation to NdFe11Co greatly enhances A_{0}^{2}. This suggests that Co is a good candidate as a substitutional element for NdFe12N.

cond-mat.mtrl-sci↗

arXiv2016

Direct theoretical evidence for weaker correlations in electron-doped and Hg-based hole-doped cuprates

Many important questions for high-$T_c$ cuprates are closely related to the insulating nature of parent compounds. While there has been intensive discussion on this issue, all arguments rely strongly on, or are closely related to, the correlation strength of the materials. Clear understanding has been seriously hampered by the absence of a direct measure of this interaction, traditionally denoted by $U$. Here, we report a first-principles estimation of $U$ for several different types of cuprates. The $U$ values clearly increase as a function of the inverse bond distance between apical oxygen and copper. Our results show that the electron-doped cuprates are less correlated than their hole-doped counterparts, which supports the Slater picture rather than the Mott picture. Further, the $U$ values significantly vary even among the hole-doped families. The correlation strengths of the Hg-cuprates are noticeably weaker than that of La$_2$CuO$_4$. Our results suggest that the strong correlation enough to induce Mott gap may not be a prerequisite for the high-$T_c$ superconductivity.

cond-mat.supr-con↗

arXiv2016

Accurate energy bands calculated by the hybrid quasiparticle self-consistent GW method implemented in the ecalj package

We have recently implemented a new version of the quasiparticle self-consistent GW (QSGW) method in the ecalj package released at http://github.com/tkotani/ecalj. Since the new version of the ecalj is numerically stable and accurate compared to the previous versions, we can perform calculations easily without being bothered with setting input parameters. Here we examine its ability to describe energy band properties, e.g., band-gap energy, eigenvalues at special points and effective mass, for variety of semiconductors and insulators. We treat C, Si, Ge, Sn, SiC (in 2H, 3C, and 4H structures), (Al, Ga, N)x(N, P, As, Pb), (Zn, Cd, Mg)x(O, S, Se, Te), SiO2, HfO2, ZrO2, SrTiO3, PbS, PbTe, MnO, NiO, and HgO. We propose that a hybrid QSGW method, where we mix 80 percent of QSGW and 20 percent of LDA, gives universally good agreement with experiments for these materials.

cond-mat.mtrl-sci↗

arXiv2016

Quasiparticle self-consistent GW calculation of Sr$_2$RuO$_4$ and SrRuO$_3$:

Using quasiparticle self-consistent $GW$ calculations, we re-examined the electronic structure of Sr$_2$RuO$_4$ and SrRuO$_3$. Our calculations show that the correlation effects beyond the conventional LDA (local density approximation) and GGA (generalized gradient approximation) are reasonably well captured by QS$GW$ self-energy without any {\it ad hoc} parameter or any ambiguity related to the double-counting and the downfolding issues. While the spectral weight transfer to the lower and upper Hubbard band is not observed, the noticeable bandwidth reduction and effective mass enhancement are obtained. Important features in the electronic structures that have been debated over the last decades such as the photoemission spectra at around $-3$ eV in Sr$_2$RuO$_4$ and the half-metallicity for SrRuO$_3$ are discussed in the light of our QS$GW$ results and in comparison with the previous studies. The promising aspects of QS$GW$ are highlighted as the first-principles calculation method to describe the moderately correlated 4$d$ transition metal oxides along with its limitations.

cond-mat.str-el↗

arXiv2015

Nitrogen as the best interstitial dopant among $X$=B, C, N, O and F for strong permanent magnet NdFe$_{11}$Ti$X$: First-principles study

We study magnetic properties of NdFe$_{11}$Ti$X$, where $X$=B, C, N, O, and F, by using the first-principles calculation based on the density functional theory. Its parent compound NdFe$_{11}$Ti has the ThMn$_{12}$ structure, which has the symmetry of space group $I4/mmm$, No. 139. The magnetization increases by doping B, C, N, O, and F at the $2b$ site of the ThMn$_{12}$ structure. The amount of the increase is larger for $X$=N, O, F than for $X$=B, C. On the other hand, the crystal field parameter $\langle r^{2} \rangle A_{0}^{2}$, which controls the axial magnetic anisotropy of the Nd $4f$ magnetic moment, depends differently on the dopant. With increase of the atomic number from $X$=B, $\langle r^{2} \rangle A_{0}^{2}$ increases, takes a maximum value for $X$=N, and then turns to decrease. This suggests that in NdFe$_{11}$Ti$X$, nitrogen is the most appropriate dopant among B, C, N, O, and F for permanent magnets in terms of magnetization and anisotropy. The above calculated properties are explained based on the detailed analysis of the electronic structures of NdFe$_{11}$Ti$X$.

cond-mat.mtrl-sci↗

arXiv2015

Robust flat bands in RCo5 (R=rare earth) compounds

The mechanism to realize the peculiar flat bands generally existing in RCo5 (R=rare earth) compounds is clarified by analyzing the first-principles band structures and the tight-binding model. These flat bands are constructed from the localized eigenstates, the existence of which is guaranteed by the partial cancelation between the intersite hopping amplitudes among the Co-3d states at the Kagome sites and those between the Kagome and honeycomb sites. Their relative positions to other bands can be controlled by varying the lattice parameters keeping their dispersion almost flat, which suggests the possibility of flat-band engineering.

cond-mat.mtrl-sci↗