arXiv subjects

Yusuke Nomura

Publications and source records attributed to Yusuke Nomura.

At least 37 records · Page 2Linked to original sources

arXiv2024

Derivation of low-energy Hamiltonians for heavy-fermion Materials

By utilizing a multi-orbital periodic Anderson model with parameters obtained from \textit{ab initio} band structure calculations, combined with degenerate perturbation theory, we derive effective Kondo-Heisenberg and spin Hamiltonians that capture the interaction among the effective magnetic moments. This derivation encompasses fluctuations via both nonmagnetic $4f^0$ and magnetic $4f^2$ virtual states, and its accuracy is confirmed through comparison with experimental data obtained from CeIn$_3$. The significant agreement observed between experimental results and theoretical predictions underscores the potential of deriving minimal models from first-principles calculations for achieving a quantitative description of $4f$ materials. Moreover, our microscopic derivation unveils the underlying origin of anisotropy in the exchange interaction between Kramers doublets, shedding light on the conditions under which this anisotropy may be weak compared to the isotropic contribution.

cond-mat.str-el↗

arXiv2024

Variational Benchmarks for Quantum Many-Body Problems

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy, and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods toward a quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible.

quant-ph↗

arXiv2024

Ab initio study on heavy-fermion behavior in LiV$_2$O$_4$: Role of Hund's coupling and stability

LiV$_2$O$_4$ is a member of the so-called $3d$ heavy fermion compounds, with effective electron mass exceeding 60 times the free electron mass, comparable to $4f$ heavy fermion compounds. The origin of the strong electron correlation in combination with its metallic character have been a subject of intense theoretical and experimental discussion, with Kondo-like physics and Mott-physics being suggested as its physical origin. Here we report state-of-the art \textit{ab initio} Density Functional Theory + dynamical mean-field theory calculations for LiV$_2$O$_4$ for the full three orbital V $t_\mathrm{2g}$ manifold, and present temperature-dependent spectral properties. We map out the phase diagram for a representative 3-orbital model system as a function of doping and interaction strength, which indicates that LiV$_2$O$_4$ is located between two orbital-selective Mott phases, giving rise to a robust strongly correlated Hund's metal behavior. At low temperature we find the emergence of a strongly renormalized sharp quasi-particle peak a few meV above the Fermi level of V $a_\mathrm{1g}$ character, in agreement with experimental reports.

cond-mat.str-el↗

arXiv2024

Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems

With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation.

cond-mat.str-el↗

arXiv2024

Variation of Electron-electron interaction in pyrochlore structures

We conduct a comprehensive \textit{ab initio} investigation of electron-electron interactions within the pyrochlore structures of R$_2$Ru$_2$O$_7$, R$_2$Ir$_2$O$_7$, Ca$_2$Ru$_2$O$_7$, and Cd$_2$Ru$_2$O$_7$, where R denotes a rare-earth element. Utilizing a multiorbital Hubbard model, we systematically explore the effects of various rare-earth elements and applied high pressure on the correlation strength in these compounds. Our calculations on the Coulomb interaction parameter $U$ and the bandwidth $W$ reveal that the chemical pressure for R$_2$Ru$_2$O$_7$ and R$_2$Ir$_2$O$_7$ leads to an unusual increase in $U/W$ ratio, hence, increase in correlation strength. Contrary to conventional understanding of bandwidth control, our study identifies that the Hubbard $U$ is more influential than the bandwidth $W$ behind the metal-insulator landscape of R$_2$Ru$_2$O$_7$ and R$_2$Ir$_2$O$_7$, leading to an interaction-controlled metal-insulator transition. We also find unexpected behavior in physical pressure. Whereas physical pressure leads to a decrease in the correlation strength $U/W$ as usual in R$_2$Ru$_2$O$_7$, the effect is notably small in Ca$_2$Ru$_2$O$_7$ and Cd$_2$Ru$_2$O$_7$, which provides an important clue to understanding unusual pressure-induced metal-insulator transition observed experimentally.

cond-mat.str-el↗

arXiv2024

A microscopic Kondo lattice model for the heavy fermion antiferromagnet CeIn$_3$

Electrons at the border of localization generate exotic states of matter across all classes of strongly correlated electron materials and many other quantum materials with emergent functionality. Heavy electron metals are a model example, in which magnetic interactions arise from the opposing limits of localized and itinerant electrons. This remarkable duality is intimately related to the emergence of a plethora of novel quantum matter states such as unconventional superconductivity, electronic-nematic states, hidden order and most recently topological states of matter such as topological Kondo insulators and Kondo semimetals and putative chiral superconductors. The outstanding challenge is that the archetypal Kondo lattice model that captures the underlying electronic dichotomy is notoriously difficult to solve for real materials. Here we show, using the prototypical strongly-correlated antiferromagnet CeIn$_3$, that a multi-orbital periodic Anderson model embedded with input from ab initio bandstructure calculations can be reduced to a simple Kondo-Heisenberg model, which captures the magnetic interactions quantitatively. We validate this tractable Hamiltonian via high-resolution neutron spectroscopy that reproduces accurately the magnetic soft modes in CeIn$_3$, which are believed to mediate unconventional superconductivity. Our study paves the way for a quantitative understanding of metallic quantum states such as unconventional superconductivity.

cond-mat.str-el↗

arXiv2023

Boltzmann machines and quantum many-body problems

Analyzing quantum many-body problems and elucidating the entangled structure of quantum states is a significant challenge common to a wide range of fields. Recently, a novel approach using machine learning was introduced to address this challenge. The idea is to "embed" nontrivial quantum correlations (quantum entanglement) into artificial neural networks. Through intensive developments, artificial neural network methods are becoming new powerful tools for analyzing quantum many-body problems. Among various artificial neural networks, this topical review focuses on Boltzmann machines and provides an overview of recent developments and applications.

cond-mat.str-el↗

arXiv2023

Ab Initio Studies on Interactions in K$_3$C$_{60}$ under High Pressure

Fullerene solids doped with alkali metals (A$_3$C$_{60}$, A = K, Rb, Cs) exhibit a superconducting transition temperature ($T_c$) as high as 40 K, and their unconventional superconducting properties have been a subject of debate. With application of high pressure on K$_3$C$_{60}$ and Rb$_3$C$_{60}$, the experiments demonstrate the decrease of $T_c$. In this paper, we focus on K$_3$C$_{60}$ and derive the structure of K$_3$C$_{60}$ under different pressures based on first-principles calculations, exploring the trends of Coulomb interactions at various pressures. By utilizing the Maximally Localized Wannier function approach, Constrained Density Functional Perturbation Theory (cDFPT), and Constrained Random Phase Approximation (cRPA), we construct a microscopic low-energy model near the Fermi level. Our results strongly indicate that, in the K$_3$C$_{60}$ system, as pressure increases, the effect of phonons is the key to intraorbital electron pairing. There is a dominance of the phonon-driven superconducting mechanism at high pressure.

cond-mat.supr-con↗

arXiv2023

Instability of Pa$\overline 3$ Cs$_{3}$C$_{60}$ at ambient pressure and superconducting state of the FCC phase

The alkali-doped fulleride Cs$_{3}$C$_{60}$, crystallized in the space group Fm$\overline 3$m or Pm$\overline 3$n, exhibits unconventional $s$-wave superconductivity under pressure with a maximum $T_c\sim 38$ K. Recently, a new primitive-cubic-structured Cs$_{3}$C$_{60}$ phase corresponding to the space group Pa$\overline 3$ has been reported (arXiv:2208.09429) and the authors observed superconductivity at ambient pressure. Using density-functional theory (DFT) calculations, we show that the proposed Pa$\overline 3$ structure is not stable under ionic relaxation, but transforms into the FCC structure. We study the normal and superconducting state of the stable FCC phase at different temperatures and volumes using DFT plus dynamical mean-field theory (DFT+DMFT) in the Nambu formalism. As temperature increases, the transition between superconductor and normal metal (Mott insulator) at small (big) volume is found to be second (first) order. The recently developed maximum entropy analytic continuation method for the anomalous-self-energy is used to study the momentum-resolved spectra and optical conductivity.

cond-mat.supr-con↗

arXiv2022

Density functional theory

Density functional theory (DFT) is an essential building block for modern theoretical physics, chemistry, and engineering, especially those concerning electronic properties. Through decades of development, various program packages for first-principles electronic structure calculation are now available. Their sophisticated interfaces allow users to apply DFT to actual systems, even without knowing the theory. It is hence becoming more and more important to recall the fundamentals of how DFT enables accurate calculations. This article attempts to provide such knowledge with a minimal overview of DFT -- its basic foundation, relations to observable electronic and nuclear dynamical properties, and some of its cutting-edge applications.

cond-mat.mtrl-sci↗

arXiv2022

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy

In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. Motion models can be used to simulate motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. We propose a deep learning probabilistic framework that generates deformation vector fields (DVFs) warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and ground truth distributions of volume and center of mass changes. With a DICE score of 0.86 and a distance between prostate contours of 1.09 mm, DAM matches and improves upon PCA-based models. The distribution overlap further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Conditioned only on a planning CT and contours of a new patient without any pre-processing, DAM can accurately predict CTs seen during following treatment sessions, which can be used for anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

physics.med-ph↗

arXiv2022

Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans

Objective: Knowledge based planning (KBP) typically involves training an end-to-end deep learning model to predict dose distributions. However, training end-to-end methods may be associated with practical limitations due to the limited size of medical datasets that are often used. To address these limitations, we propose a content based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Approach: Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github. Main Results: The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available plans and clinical plans from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network. Significance: Applying CBIR to inform subsequent treatment planning potentially addresses many limitations associated with end-to-end KBP. Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works, potentially through methods like the MetaPlanner framework.

cs.CV↗

arXiv2022

Doping asymmetry and layer-selective metal-insulator transition in trilayer K$_{3+x}$C$_{60}$

Thin films provide a versatile platform to tune electron correlations and explore new physics in strongly correlated materials. Epitaxially grown thin films of the alkali-doped fulleride K$_{3+x}$C$_{60}$, for example, exhibit %various intriguing phenomena, including Mott transitions and superconductivity, depending on dimensionality and doping. Surprisingly, in the trilayer case, a strong electron-hole doping asymmetry has been observed in the superconducting phase, which is absent in the three-dimensional bulk limit. Using density-functional theory plus dynamical mean-field theory, we show that this doping asymmetry results from a substantial charge reshuffling from the top layer to the middle layer. While the nominal filling per fullerene is close to $n=3$, the top layer rapidly switches to an $n=2$ insulating state upon hole doping, which \textcolor{black}{implies a} doping asymmetry of the superconducting gap. The interlayer charge transfer and layer-selective metal-insulator transition result from the interplay between crystal field splittings, strong Coulomb interactions, and an effectively negative Hund coupling. This peculiar charge reshuffling is absent in the monolayer system, which is an $n=3$ Mott insulator, as expected from the nominal filling.

cond-mat.str-el↗

arXiv2022

$\textit{Ab initio}$ Materials Design of Superconductivity in $d^9$ Nickelates

Motivated by the recent theoretical materials design of superconducting $d^9$ nickelates for which the charge transfer from the NiO$_2$ to the block layer is completely suppressed [M. Hirayama $\textit{et al.}$, Phys. Rev. B $\textbf{101}$, 075107 (2020)], we perform a calculation based on the dynamical vertex approximation and obtain the phase diagram of RbCa$_2$NiO$_3$ and $A_2$NiO$_2$Br$_2$ where $A$ is a cation with a valence of 2.5+. We show that the phase diagram of these nickelates exhibits the same essential features as those found in cuprates. Namely, superconductivity appears upon hole-doping into an antiferromagnetic Mott insulator, and the superconducting transition temperature shows a dome-like shape. This demonstrates that the electron correlations play an essential role in nickelate superconductors and we can control them by changing block layers.

cond-mat.supr-con↗

arXiv2022

Investigating Network Parameters in Neural-Network Quantum States

Recently, quantum-state representation using artificial neural networks has started to be recognized as a powerful tool. However, due to the black-box nature of machine learning, it is difficult to analyze what machine learns or why it is powerful. Here, by applying one of the simplest neural networks, the restricted Boltzmann machine (RBM), to the ground-state representation of the one-dimensional (1D) transverse-field Ising (TFI) model, we make an attempt to directly analyze the optimized network parameters. In the RBM optimization, a zero-temperature quantum state is mapped onto a finite-temperature classical state of the extended Ising spins that constitute the RBM. We find that the quantum phase transition from the ordered phase to the disordered phase in the 1D TFI model by increasing the transverse field is clearly reflected in the behaviors of the optimized RBM parameters and hence in the finite-temperature phase diagram of the classical RBM Ising system. The present finding of a correspondence between the neural-network parameters and quantum phases suggests that a careful investigation of the neural-network parameters may provide a new route to extracting nontrivial physical insights from the neural-network wave functions.

quant-ph↗

arXiv2022

Superconductivity in infinite-layer nickelates

The recent discovery of the superconductivity in the doped infinite layer nickelates $R$NiO$_2$ ($R$=La, Pr, Nd) is of great interest since the nickelates are isostructural to doped (Ca,Sr)CuO$_2$ having superconducting transition temperature ($T_{\rm c}$) of about 110 K. Verifying the commonalities and differences between these oxides will certainly give a new insight into the mechanism of high $T_{\rm c}$ superconductivity in correlated electron systems. In this paper, we review experimental and theoretical works on this new superconductor and discuss the future perspectives for the "nickel age" of superconductivity.

cond-mat.supr-con↗

arXiv2022

Fermi surface expansion above critical temperature in a Hund ferromagnet

Using a cluster extension of the dynamical mean-field theory, we show that strongly correlated metals subject to Hund's physics exhibit significant electronic structure modulations above magnetic transition temperatures. In particular, in a ferromagnet having a large local moment due to Hund's coupling (Hund's ferromagnet), the Fermi surface expands even above the Curie temperature ($T_{\rm C}$) as if a spin polarization occurred. Behind this phenomenon, effective ``Hund's physics'' works in momentum space, originating from ferromagnetic fluctuations in the strong coupling regime. The resulting significantly momentum-dependent (spatially nonlocal) electron correlations induce an electronic structure reconstruction involving a Fermi-surface volume change and a redistribution of the momentum-space occupation. Our finding will give a deeper insight into the physics of Hund's ferromagnets above $T_{\rm C}$.

cond-mat.str-el↗

arXiv2022

$\textit{Ab initio}$ downfolding based on the GW approximation for infinite-layer nickelates

We derive an effective three-orbital model for the infinite-layer nickelates based on the band structure obtained by the GW approximation (GWA), where we consider the Ni 3$d_{x^2-y^2}$ and O 2$p$ orbitals forming the $σ$-bond. In the GWA, the self-energy correction to the local density approximation (LDA) increases the energy difference between Ni $3d_{x^2-y^2}$ and O $2p$, which reduces the bandwidth of the antibonding 3$d_{x^2-y^2}$ orbitals. The isolation of the Ni $3d_{x^2-y^2}$ around the Fermi level suppresses the screening effect. As a result, the correlation effect becomes more significant than that in the model constructed by the LDA-based downfolding. Furthermore, the Mott-Hubbard type character is enhanced in the GWA-based effective model, because the charge-transfer energy increases more rapidly compared to the increase in the interaction parameters.

cond-mat.str-el↗