arXiv subjects

Kohei Shinohara

Publications and source records attributed to Kohei Shinohara.

12 recordsLinked to original sources

arXiv2026

Conserved quantities and ensemble measure for Martyna--Tobias--Klein barostats with restricted cell degrees of freedom

We derive the conserved energy-like quantity and ensemble measure for Martyna--Tobias--Klein (MTK) barostats in which only a restricted subset of the cell degrees of freedom are active. In the standard fully anisotropic MTK formulation, the number of barostat degrees of freedom is $d^{2}$, where $d$ is the spatial dimension. When only $n_c$ axes of the cell matrix are allowed to fluctuate, the conserved energy-like quantity retains the same functional form but with $d^{2}$ replaced by $n_c$ in every term that counts barostat degrees of freedom. The derivation builds on the generalized Liouville framework for non-Hamiltonian systems and the existing MTK integration machinery. We verify that this quantity is exactly conserved, show that the resulting dynamics samples the isothermal--isobaric ensemble restricted to the submanifold of cell shapes in which inactive components are held fixed, and provide a complete Liouville-operator-based integration scheme for the masked MTK variant.

physics.comp-ph↗

arXiv2026

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional

Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional theory (DFT). However, most existing uMLIPs are trained on Perdew--Burke--Ernzerhof (PBE) generalized gradient approximation (GGA) data and are therefore fundamentally limited by PBE-level accuracy. In this paper, we argue that better zero-shot predictions versus experiments must be an explicit design target for uMLIPs and present PFP v8, a uMLIP available on the Matlantis service that overcomes the inherent limitations of the PBE functional by being trained to reproduce the regularized-restored strongly constrained and appropriately normed (r2SCAN) meta-GGA potential-energy surface across a wide range of chemical domains. Without requiring domain-specific fine-tuning, PFP v8 delivers systematically improved agreement with experimental data or high-accuracy references for crystals, molecules, and surfaces, outperforming PBE-based DFT calculations. Crucially, in long-time molecular dynamics simulations that are computationally impractical with DFT, PFP v8 predicts melting points with an average error of approximately 130 K, halving the error relative to PBE-trained models. These results establish that uMLIPs can move beyond the limitations of their training approximations and achieve substantially improved agreement with experiment across diverse chemical domains, further narrowing the gap between simulation and reality.

physics.chem-ph↗

arXiv2026

Systematic Magnetic Structure Generation Based on Oriented Spin Space Groups: Formulation, Applications, and High-Throughput First-Principles Calculations

We propose a framework for generating magnetic structures, inspired by the concept of oriented spin space groups (SSGs): magnetic structures are first generated as totally symmetric representations of an SSG and are then rotated such that they belong to the maximal magnetic space group of the SSG, which we term spin-symmetry-adapted (SSA) structures and oriented SSA structures, respectively. This is a natural framework to enforce fixed magnetic moment magnitudes on the symmetry-equivalent sites as well as to exploit the spin-orbit coupling (SOC)-induced hierarchy of energy scales. To examine the present scheme, we analyze the MAGNDATA database and find that 77% of the reported structures are reproducible at the SSG level, among which 82% are fully reproduced within the oriented SSG scheme, regardless of their spin-only group types or propagation vectors. To quantitatively assess computational and predictive performance, we perform spin density functional theory calculations for 283 materials, first carrying out self-consistent calculations for SSA structures without SOC, followed by fixed-charge calculations including SOC for the descendant oriented SSA structures. The experimental magnetic structures are reproduced as energetically most stable in 82% of cases at the SSG level without SOC and in 76% of cases at the oriented SSG level with SOC, showing that the fixed-charge scheme enables accurate evaluation of SOC-induced energy differences at low computational cost. The characteristic energy scale among oriented SSA structures is only $\sim$0.29 meV per magnetic atom, about 300 times smaller than that of distinct SSA structures. These results demonstrate that oriented SSG-based enumeration, combined with the two-step calculations for SSA and oriented SSA structures, provides an efficient and robust route for large-scale magnetic-structure prediction.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models

The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms

Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicomponent systems remains a significant challenge. Here, we propose an improvement of genetic algorithm (GA) -based CSP method using a universal NNP. Our GA-based methods are designed to efficiently expand convex hull volumes while preserving the diversity of crystal structures. Our hull-informed filtering and elitist-selection procedures incorporate an aging mechanism that prioritizes recently improved compositions. We also employ niching to prevent convergence to a small set of stoichiometries, thereby preserving a diverse, high-quality population. Our evaluation shows that the present method outperforms the symmetry-aware random structure generation and existing CSP methods, achieving a larger convex hull with fewer trials. We demonstrated that our approach, combined with the developed universal NNP (PFP), can accurately reproduce and explore phase diagrams obtained through DFT calculations; this indicates the validity of PFP across a wide range of crystal structures and element combinations. This study, which integrates a universal NNP with a GA-based CSP method, highlights the promise of these methods in materials discovery.

cond-mat.mtrl-sci↗

arXiv2024

Symmetry analysis with spin crystallographic groups: Disentangling effects free of spin-orbit coupling in emergent electromagnetism

Recent studies identified spin-order-driven phenomena such as spin-charge interconversion without relying on the relativistic spin-orbit interaction. Those physical properties can be prominent in systems containing light magnetic atoms due to sizable exchange splitting and may pave the way for realization of giant responses correlated with the spin degree of freedom. In this paper, we present a systematic symmetry analysis based on the spin crystallographic groups and identify the physical property of a vast number of magnetic materials up to 1500 in total. By decoupling the spin and orbital degrees of freedom, our analysis enables us to take a closer look into the relation between the dimensionality of spin structures and the resultant physical properties and to identify the spin and orbital contributions separately. In stark contrast to the established analysis with magnetic space groups, the spin crystallographic group manifests richer symmetry including spin-translation symmetry and leads to emergent responses. For representative examples, we discuss the geometrical nature of the anomalous Hall effect and magnetoelectric effect and classify the spin Hall effect arising from the nonrelativistic spin-charge coupling. Using the power of computational analysis, we apply our symmetry analysis to a wide range of magnets, encompassing complex magnets such as those with noncoplanar spin structures as well as collinear and coplanar magnets. We identify emergent multipoles relevant to physical responses and argue that our method provides a systematic tool for exploring sizable electromagnetic responses driven by spin order.

cond-mat.mtrl-sci↗

arXiv2024

$\texttt{Spglib}$: a software library for crystal symmetry search

A computer algorithm to search symmetries of crystal structures as implemented in the \texttt{spglib} code is described. An iterative algorithm is employed to robustly identify space group types tolerating a certain amount of distortion in the crystal structures. The source code is distributed under the 3-Clause BSD License, a permissive open-source software license. This paper focuses on the algorithm for identifying the space group symmetry of the crystal structures.

cond-mat.mtrl-sci↗

arXiv2024

Layer group classification of two-dimensional materials

The symmetry of a crystal structure with a three-dimensional (3D) lattice can be classified by one of the 230 space group types. For some types of crystals, e.g. crystalline films, surfaces, or planar interfaces, it is more appropriate to assume a 2D lattice. With this assumption, the structure can be classified by one of the 80 layer group types. We have implemented an algorithm to determine the layer group type of a 3D structure with a 2D lattice, and applied it to more than 15,000 monolayer structures in the Computational 2D Materials (C2DB) database. We compare the classification of monolayers by layer groups and space groups, respectively. The latter is defined as the space group of the 3D bulk structure obtained by repeating the monolayer periodically in the direction perpendicular to the 2D lattice (AA-stacking). By this correspondence, nine pairs of layer group types are mapped to the same space group type due to the inability of the space group to distinguish the in-plane and out-of-plane axes. In total 18% of the monolayers in the C2DB belong to one of these layer group pairs and are thus not properly classified by the space group type.

cond-mat.mtrl-sci↗

arXiv2023

Algorithm for spin symmetry operation search

A spin space group provides a suitable way to fully exploit the symmetry of a spin arrangement with a negligible spin-orbit coupling. There has been a growing interest in applying spin symmetry analysis with the spin space group in the field of magnetism. However, there is no established algorithm to search for spin symmetry operations of the spin space group. This paper presents an exhaustive algorithm for determining spin symmetry operations of commensurate spin arrangements. The present algorithm searches for spin symmetry operations from the symmetry operations of a corresponding nonmagnetic crystal structure and determines their spin-rotation parts by solving a Procrustes problem. An implementation is distributed under a permissive free software license in spinspg v0.1.1: https://github.com/spglib/spinspg.

cond-mat.mtrl-sci↗

arXiv2023

Algorithms for magnetic symmetry operation search and identification of magnetic space group from magnetic crystal structure

A crystal symmetry search is crucial for computational crystallography and materials science. Although algorithms and implementations for the crystal symmetry search have been developed, their extension to magnetic space groups (MSGs) remains limited. In this paper, algorithms for determining magnetic symmetry operations of magnetic crystal structures, identifying magnetic space-group types of given MSGs, searching for transformations to a BNS setting, and symmetrizing the magnetic crystal structures using the MSGs are presented. The determination of magnetic symmetry operations is numerically stable and is implemented with minimal modifications from the existing crystal symmetry search. Magnetic space-group types and transformations to the BNS setting are identified by a two-step approach combining space-group type identification and the use of affine normalizers. Point coordinates and magnetic moments of the magnetic crystal structures are symmetrized by projection operators for the MSGs. An implementation is distributed with a permissive free software license in spglib v2.0.2: https://github.com/spglib/spglib.

cond-mat.mtrl-sci↗

arXiv2021

Finding well-optimized special quasirandom structures with decision diagram

The advanced data structure of the zero-suppressed binary decision diagram (ZDD) enables us to efficiently enumerate nonequivalent substitutional structures. Not only can the ZDD store a vast number of structures in a compressed manner, but also can a set of structures satisfying given constraints be extracted from the ZDD efficiently. Here, we present a ZDD-based efficient algorithm for exhaustively searching for special quasirandom structures (SQSs) that mimic the perfectly random substitutional structure. We demonstrate that the current approach can extract only a tiny number of SQSs from a ZDD composed of many substitutional structures (>$10^{12}$). As a result, we find SQSs that are optimized better than those proposed in the literature. A series of SQSs should be helpful for estimating the properties of substitutional solid solutions. Furthermore, the present ZDD-based algorithm should be useful for applying the ZDD to the other structure enumeration problems.

physics.comp-ph↗

arXiv2020

Derivative structure enumeration using binary decision diagram

A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role in searching for the ground states in multicomponent systems. However, it is computationally hard to enumerate derivative structures if the number of derivative structures of a target system becomes huge. In the present study, we introduce a novel compact data structure of the zero-suppressed binary decision diagram (ZDD) to enumerate derivative structures much more efficiently. The present study shows its simple applications to the enumeration of structures derived from the face-centered cubic and hexagonal close-packed lattices in binary, ternary, and quaternary systems. The present ZDD-based procedure should significantly contribute not only to various computational approaches based on the derivative structures but also to a wide range of combinatorial issues in physics and materials science.

physics.comp-ph↗