arXiv subjects

Kousuke Nakano

Publications and source records attributed to Kousuke Nakano.

At least 19 recordsLinked to original sources

arXiv2026

Quadrupolar phase transition in superconducting lanthanum hydride

Lanthanum hydride (LaH$_{10}$) has been widely studied for its high superconducting critical temperature of 250 K at about 170 GPa pressure. Although the structural ${R\bar{3}m}$-to-${Fm\bar{3}m}$ transition under pressure connected to the emergence of the superconducting phase in this material is broadly understood, the detailed characterization of its nature and its order parameter are still missing. By applying the cluster multipole moment analysis to the hydrogen sublattice, we reveal that this transition is triggered by a quadrupolar $T_{2g}$ order parameter, and we provide evidence for its weak first-order nature. By performing path integral molecular dynamics coupled to a message-passing atomic cluster expansion (MACE) neural network potential, trained on Perdew-Burke-Ernzerhof (PBE) density functional theory configurations, we show that the collapse of the order parameter at the transition is simultaneously associated with the discontinuous softening of the optical $T_{2g}$ phonons. Their symmetry lets them carry a non-negligible electron-phonon coupling in LaH$_{10}$, while the weak first-order nature of the transition makes them soft. The presence of structural instabilities with low-frequency quadrupolar distortions can be a key ingredient to enhance superconductivity in superhydrides and provides guidance for the discovery of new high-$T_c$ superconductors in hydrogen-rich compounds.

cond-mat.supr-con↗

arXiv2026

jQMC: A JAX-based ab initio quantum Monte Carlo package designed for GPU-accelerated computing

We present jQMC, a Python-based computational package for {\it ab initio} Quantum Monte Carlo (QMC) simulations, designed for modern GPU-accelerated computing environments. jQMC implements two well-established QMC algorithms: Variational Monte Carlo (VMC) and the lattice-regularized variant of Diffusion Monte Carlo (LRDMC). The employed wave function is a Jastrow factor combined with the antisymmetrized geminal power with spin-singlet and spin-triplet pairings, which contains the single Slater determinant as its special lowest-rank case. The wave function can be initialized from external Hartree-Fock/Density Functional Theory calculations through the TREX-IO library (a common wave-function format across electronic-structure packages) and optimized by stochastic reconfiguration and linear-method energy minimization. One of the prominent features of jQMC is its use of JAX, which enables automatic differentiation for wave function optimization and atomic force calculations, and allows the main QMC algorithms to be Just-In-Time (JIT) compiled and portable across CPU and GPU. jQMC is vectorized over walkers at the top level of the QMC algorithms, providing efficient intra-GPU~(CPU) vectorization. The multi-GPU~(CPU) parallelization is also supported through MPI and JAX sharding. To assess the practical performance of this implementation, we benchmarked jQMC performance on NVIDIA GPUs (A100 and H100) and analyzed CUDA kernels. For the test cases analyzed here, with system sizes up to 160 electrons, the current version of jQMC is faster than TurboRVB, a Fortran90 code implementing the same algorithms and wave functions, once jQMC is run on GPUs. In terms of wall-time, the gain can reach an order of magnitude for VMC, while it is more moderate for LRDMC.

physics.chem-ph↗

arXiv2026

Assessing the impact of nodal surface optimization in fixed-node diffusion Monte Carlo on non-covalent interactions

Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed benchmark methods for noncovalent interactions (NCIs). However, recent studies have reported notable discrepancies across several hydrogen-bonded and dispersion-dominated systems, raising questions on the accuracy of the approximations underlying each approach. In DMC, the dominant error is expected to stem from the fixed-node approximation, where the nodal surface is typically taken from a single Slater determinant derived from a density functional theory or Hartree-Fock calculation. In this work, we assess the impact of nodal surface optimization on DMC predictions for 12 compounds spanning diverse NCIs, using a recently proposed antisymmetrized geminal power ansatz with natural orbitals. We find improved agreement with CCSD(T) for hydrogen-bonded systems, while having negligible effect for dispersion-dominated systems. These results provide a practical and computationally efficient route to resolving discrepancies in hydrogen-bonded interactions, while offering insight into the remaining differences in dispersion-dominated systems.

physics.chem-ph↗

arXiv2026

The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks.

physics.chem-ph↗

arXiv2025

Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique

Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schr{\"o}dinger equation, suitable for modern many-core supercomputer architectures. Despite its potential, one of the major drawbacks that still hinders QMC applications, especially when targeting dynamical properties of large systems or extensive datasets, is the lack of an affordable method to compute atomic forces that are consistent with the corresponding potential energy surfaces (PESs), also known as unbiased atomic forces. Recently, one of the authors in the present paper proposed a way to obtain unbiased forces with the Jastrow-correlated Slater determinant ansatz, where the determinant part is frozen to the values obtained by a mean-field method, such as Density Functional Theory. However, the proposed method has a significant drawback for its applications: for a system with $N$ nuclei, one requires 6$N$ additional DFT calculations to get unbiased forces. This paper presents a way to replace the 6$N$ DFT calculations with a single coupled-perturbed Kohn-Sham calculation, following the so-called Lagrangian technique established in quantum chemistry. We also demonstrate that the developed unbiased VMC force calculation improves not only the consistency with PESs, but also its accuracy, by investigating three molecules from the rMD17 benchmark set, and comparing the unbiased VMC forces with those obtained by CCSD(T) calculations. We found that the bare VMC forces are biased from the CCSD(T) ones, while the unbiased ones give values closer to those of the CCSD(T) ones. Our benchmark test also reveals that the unbiased VMC forces yield very consistent values with hybrid and meta GGAs, but do not necessarily yield values that are very close to those of CCSD(T).

physics.chem-ph↗

arXiv2025

Load-Balanced Diffusion Monte Carlo Method with Lattice Regularization

Ab initio quantum Monte Carlo (QMC) is a stochastic approach for solving the many-body Schr\"odinger equation without resorting to one-body approximations. QMC algorithms are readily parallelizable via ensembles of $N_w$ walkers, making them well suited to large-scale high-performance computing. Among the QMC techniques, Diffusion Monte Carlo (DMC) is widely regarded as the most reliable, since it provides the projection onto the ground state of a given Hamiltonian under the fixed-node approximation. One practical realization of DMC is the Lattice Regularized Diffusion Monte Carlo (LRDMC) method, which discretizes the Hamiltonian within the Green's Function Monte Carlo framework. DMC methods - including LRDMC - employ the so-called branching technique to stabilize walker weights and populations. At the branching step, walkers must be synchronized globally; any imbalance in per-walker workload can leave CPU or GPU cores idle, thereby degrading overall hardware utilization. The conventional LRDMC algorithm intrinsically suffers from such load imbalance, which grows as $\log(N_w)$, rendering it less efficient on modern parallel architectures. In this work, we present an LRDMC algorithm that inherently addresses the load imbalance issue and achieves significantly improved weak-scaling parallel efficiency. Using the binding energy calculation of a water-methane complex as a test case, we demonstrated that the conventional and load-balanced LRDMC algorithms yield consistent results. Furthermore, by utilizing the Leonardo supercomputer equipped with NVIDIA A100 GPUs, we demonstrated that the load-balanced LRDMC algorithm can maintain extremely high parallel efficiency ($\sim$98\%) up to 512 GPUs (corresponding to $N_{\rm w}= 51200$), together with a speedup of $\times~1.24$ if directly compared with the conventional LRDMC algorithm with the same number of walkers.

physics.chem-ph↗

arXiv2025

Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo

Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and for system sizes previously unattainable. VMC training sets are often based on partially optimized wave functions (WFs) to circumvent expensive energy optimizations of the whole set of WF parameters. However, frozen variational parameters lead to VMC forces and pressures not consistent with the underlying potential energy surface, a bias called the self-consistency error (SCE). Here, we demonstrate how the SCE can spoil the accuracy of MLIPs trained on these data, taking high-pressure hydrogen as test case. We then apply a recently introduced SCE correction [ Phys. Rev. B 109, 205151 (2024)] to generate unbiased VMC training sets based on a Jastrow-correlated single determinant WF with frozen Kohn-Sham orbitals. The MLIPs generated within this framework are significantly improved and can approach in quality those trained on datasets built with fully optimized WFs. Our conclusions are further supported by MD simulations, which show how MLIPs trained on SCE-corrected datasets systematically yield more reliable physical observables. Our framework opens the possibility of constructing extended high-quality training sets with VMC.

cond-mat.str-el↗

arXiv2025

Hydrogen liquid-liquid transition from first principles and machine learning

The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental topic touching domains from planetary science to materials modeling. Yet, the nature of the LLT is still under debate. To resolve it, numerical simulations must cover length and time scales spanning several orders of magnitude. We overcome these size and time limitations by constructing a fast and accurate machine-learning interatomic potential (MLIP) built on the MACE neural network architecture. The MLIP is trained on Perdew-Burke-Ernzerhof (PBE) density functional calculations and uses a modified loss function correcting for an energy bias in the molecular phase. Classical and path-integral molecular dynamics driven by this MLIP show that the LLT is always supercritical above the melting temperature. The position of the corresponding Widom line agrees with previous ab initio PBE calculations, which in contrast predicted a first-order LLT. According to our calculations, the crossover line becomes a first-order transition only inside the molecular crystal region. These results call for a reconsideration of the LLT picture previously drawn.

cond-mat.dis-nn↗

arXiv2025

A Denser Hydrogen Inferred from First-Principles Simulations Challenges Jupiter's Interior Models

First-principle modeling of dense hydrogen is crucial in materials and planetary sciences. Despite its apparent simplicity, predicting the ionic and electronic structure of hydrogen is a formidable challenge, and it is connected with the insulator-to-metal transition, a century-old problem in condensed matter. Accurate simulations of liquid hydrogen are also essential for modeling gas giant planets. Here we perform an exhaustive study of the equation of state of hydrogen using Density Functional Theory and quantum Monte Carlo simulations. We find that the pressure predicted by Density Functional Theory may vary qualitatively when using different functionals. The predictive power of first-principle simulations is restored by validating each functional against higher-level wavefunction theories, represented by computationally intensive variational and diffusion Monte Carlo calculations. Our simulations provide evidence that hydrogen is denser at planetary conditions, compared to currently used equations of state. For Jupiter, this implies a lower bulk metallicity (i.e., a smaller mass of heavy elements). Our results further amplify the inconsistency between Jupiter's atmospheric metallicity measured by the Galileo probe and the envelope metallicity inferred from interior models.

astro-ph.EP↗

arXiv2024

Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection method, is often considered minimally affected by basis set biases. Here, we show that this assumption is not always valid. While the relative error introduced by a small basis set in the total FN-DMC energy is minor, it can become significant in binding energy ($E_{\rm b}$) evaluations of weakly interacting systems. We systematically investigated BSIEs in FN-DMC-based binding energy ($E_{\rm b}$) evaluations using the A24 dataset, a well-known benchmark set of 24 non-covalently bound dimers. Contrary to common expectations, we found that BSIEs in FN-DMC evaluations of $E_{\rm b}$ are indeed significant when small localized basis sets, such as cc-pVDZ, are employed. We observed that BSIEs are larger in dimers with hydrogen-bonding interactions and smaller in dispersion-dominated interactions. We also found that augmenting the basis sets with diffuse orbitals, using counterpoise (CP) correction, or both, effectively mitigates BSIEs.

physics.chem-ph↗

arXiv2024

Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons

Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in fermionic systems the projection needs to be carried out while imposing anti-symmetric constraints, which is a nondeterministic polynomial hard problem. In practice, therefore, the application of DMC on electronic structure problems is made by employing the fixed-node (FN) approximation, consisting of performing DMC with the constraint of having a fixed predefined nodal surface. How do we get the nodal surface? The typical approach, applied in systems having up to hundreds, or even thousands of electrons, is to obtain the nodal surface from a preliminary mean-field approach (typically, a density functional theory calculation) used to obtain a single Slater determinant. This is known as {\emph{single reference}}. In this paper, we propose a new approach, applicable to systems as large as the C$_{60}$ fullerene, which improves the nodes by going beyond the single reference. In practice, we employ an implicitly multireference ansatz (Antisymmetrized Geminal power wavefunction constraint with molecular orbitals), initialized on the preliminary mean-field approach, which is relaxed by optimizing a few parameters of the wave function determining the nodal surface by minimizing the FN-DMC energy. We highlight the improvements of the proposed approach over the standard single reference method on several examples and, where feasible, the computational gain over the standard multireference ansatz, which makes the methods applicable to large systems. We also show that physical properties relying on relative energies, such as binding energies, are affordable and reliable within the proposed scheme.

physics.comp-ph↗

arXiv2023

Efficient calculation of unbiased atomic forces in ab initio Variational Monte Carlo

Ab initio quantum Monte Carlo (QMC) is a state-of-the-art numerical approach for evaluating accurate expectation values of many-body wavefunctions. However, one of the major drawbacks that still hinders widespread QMC applications is the lack of an affordable scheme to compute unbiased atomic forces. In this study, we propose a very efficient method to obtain unbiased atomic forces and pressures in the Variational Monte Carlo (VMC) framework with the Jastrow-correlated Slater determinant ansatz, exploiting the gauge-invariant and locality properties of its geminal representation. We demonstrate the effectiveness of our method for H$_2$ and Cl$_2$ molecules and for the cubic boron nitride crystal. Our framework has a better algorithmic scaling with the system size than the traditional finite-difference method, and, in practical applications, is as efficient as single-point VMC calculations. Thus, it paves the way to study dynamical properties of materials, such as phonons, and is beneficial for pursuing more reliable machine-learning interatomic potentials based on unbiased VMC forces.

cond-mat.mtrl-sci↗

arXiv2023

TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods

TurboGenius is an open-source Python package designed to fully control ab initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one to perform high-throughput calculations combined with TurboRVB [K. Nakano et al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the TurboGenius package and showcases several results obtained in a high-throughput mode. For the purpose of performing high-throughput calculations with TurboGenius, we implemented another open-source Python package, TurboWorkflows, that enables one to construct simple workflows using TurboGenius. We demonstrate its effectiveness by performing (1) validations of density functional theory (DFT) and QMC drivers as implemented in the TurboRVB package and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several data sets. For (1), we checked inter-package consistencies between TurboRVB and other established quantum chemistry packages. By doing so, we confirmed that DFT energies obtained by PySCF are consistent with those obtained by TurboRVB within the local density approximation (LDA), and that Hartree-Fock (HF) energies obtained by PySCF and Quantum Package are consistent with variational Monte Carlo energies obtained by TurboRVB with the HF wavefunctions. These validation tests constitute a further reliability check of the TurboRVB package. For (2), we benchmarked atomization energies of the Gaussian-2 set, binding energies of the S22, A24, and SCAI sets, and equilibrium lattice parameters of 12 cubic crystals using DMC calculations. We found that, for all compounds analyzed here, the DMC calculations with the LDA nodal surface give satisfactory results, i.e., consistent either with high-level computational or with experimental reference values.

physics.comp-ph↗

arXiv2023

Towards chemical accuracy using the Jastrow correlated antisymmetrized geminal power ansatz

Herein, we report accurate atomization energy calculations for 55 molecules in the Gaussian-2 (G2) set using lattice regularized diffusion Monte Carlo (LRDMC). We compare the Jastrow-Slater determinant ansatz with a more flexible JsAGPs (Jastrow correlated antisymmetrized geminal power with singlet correlation) ansatz. AGPs is built from pairing functions, which explicitly include pairwise correlations among electrons and hence, this ansatz is expected to be more efficient in recovering the correlation energy. The AGPs wave functions are first optimized at the variational Monte Carlo (VMC) level, which includes both the Jastrow factor and the nodal surface optimization. This is followed by the LRDMC projection of the ansatz. Remarkably, for many molecules, the LRDMC atomization energies obtained using the JsAGPs ansatz reach chemical accuracy ($\sim$1 kcal/mol) and for most other molecules, the atomization energies are accurate within $\sim$5 kcal/mol. We obtained a mean absolute deviation of 1.6 kcal/mol with JsAGPs and 3.2 kcal/mol with JDFT (Jastrow factor + Slater determinant with DFT orbitals) ansatz. This work shows the effectiveness of the flexible AGPs ansatz for atomization energy calculations and electronic structure simulations in general.

physics.chem-ph↗

arXiv2023

Principal deuterium Hugoniot via Quantum Monte Carlo and $\Delta$-learning

We present a study of the principal deuterium Hugoniot for pressures up to $150$ GPa, using Machine Learning potentials (MLPs) trained with Quantum Monte Carlo (QMC) energies, forces and pressures. In particular, we adopted a recently proposed workflow based on the combination of Gaussian kernel regression and $\Delta$-learning. By fully taking advantage of this method, we explicitly considered finite-temperature electrons in the dynamics, whose effects are highly relevant for temperatures above $10$ kK. The Hugoniot curve obtained by our MLPs shows a good agreement with the most recent experiments, particularly in the region below 60 GPa. At larger pressures, our Hugoniot curve is slightly more compressible than the one yielded by experiments, whose uncertainties generally increase, however, with pressure. Our work demonstrates that QMC can be successfully combined with $\Delta$-learning to deploy reliable MLPs for complex extended systems across different thermodynamic conditions, by keeping the QMC precision at the computational cost of a mean-field calculation.

cond-mat.str-el↗

arXiv2022

Topological data analysis for revealing structural origin of density anomalies in silica glass

Topological data analysis (TDA) is a new emerging and powerful tool to understand the medium range structure ordering of multi-scale data. This study investigates the density anomalies observed during cooling of liquid silica from topological point of view using TDA. The density of liquid silica does not monotonically increase during cooling; it instead shows a maximum and minimum. Despite tremendous efforts, the structural origin of these density anomalies is not clearly understood. Our approach reveals that the one-dimensional topology of the -Si-Si- network changes at the temperatures at which the maximum and minimum densities are observed in our MD simulations, while those of the -O-O- and -Si-O- networks change at lower temperatures. These results are also supported by conventional ring analysis. Our work demonstrates the value of new topological techniques in understanding the transitions in glassy materials and sheds light on the characterization of glass-liquid transitions.

cond-mat.mtrl-sci↗

arXiv2022

Potential high-$T_{c}$ superconductivity in YCeH$_{x}$ and LaCeH$_{x}$ under pressure

Lanthanum, yttrium, and cerium hydrides are the three most well-known superconducting binary hydrides (La-H, Y-H, and Ce-H systems), which have gained great attention in both theoretical and experimental studies. Recent studies have shown that ternary hydrides composed of lanthanum and yttrium can achieve high superconductivity around 253 K. In this study we employ the evolutionary-algorithm-based crystal structure prediction (CSP) method and first-principles calculations to investigate the stability and superconductivity of ternary hydrides composed of (Y, Ce) and (La, Ce) under high pressure. Our calculations show that there are multiple stable phases in Y-Ce-H and La-Ce-H systems, among which $P4/mmm$-YCeH$_{8}$, $P\bar{6}m2$-YCeH$_{18}$, $R\bar{3}m$-YCeH$_{20}$, $P4/mmm$-LaCeH$_{8}$, and $R\bar{3}m$-LaCeH$_{20}$ possessing H$_{18}$, H$_{29}$ and H$_{32}$ clathrate structures can maintain both the thermodynamic and lattice-dynamic stabilities. In addition, we also find that these phases also maintain a strong resistance to decomposition at high temperature. Electron-phonon coupling calculations show that only three of these five phases can exhibit high-temperature superconductivity. The superconducting transition temperatures ($T_\mathrm{c}$) of $R\bar{3}m$-YCeH$_{20}$, $R\bar{3}m$-LaCeH$_{20}$, and $P\bar{6}m2$-YCeH$_{18}$ are predicted using the Allen-Dynes-modified McMillan formula to be 122 K at 300 GPa, 116 K at 250 GPa, and 173 K at 150 GPa, respectively. Moreover, the pressure to stabilize $P\bar{6}m2$-YCeH$_{18}$ can be lowered to 150 GPa, suggesting an accessible condition for its high-pressure synthesis.

cond-mat.supr-con↗

arXiv2022

Ab-initio-based Interface Modeling and Statistical Analysis for Estimate of the Water Contact Angle on a Metallic Cu(111) Surface

Controlling the water contact angle on a surface is important for regulating its wettability in industrial applications, which involves developing ab initio prediction scheme of accurately predicting the angle. The scheme requires structural models for the adsorption of liquid molecules on a surface, but their reliability depend on whether the surfaces comprise insulating or metallic materials. Previous ab initio studies have focused on the estimation of the water contact angle on insulators, where the periodic-honeycomb array of water molecules was adopted as the adsorption model for the water on the insulating surface and succeeded in the insulating cases. This study, however, focus on the water contact angle on a metallic surface, and propose a simple ab initio based estimation scheme. We not only adopt the previously proposed structural modeling based on the periodic-honyecomb array, but also consider an ensemble of isolated water oligomers that have different molecular coverage (ML) values. We established a statistic model to predict a contact angle of the water wetting on a Cu(111) surface: The coverage-dependent contact angles obtained from each of the isolated clusters was fit to a quadratic regression, and the contact angle was interpolated by referring to a ML value of water layer in literature. This interpolated value lay within the deviation of experimental angles. In addition, the Boltzmann-average over the isolated clusters was found to agree well with the interpolated one. This indicates that the Boltzmann-average is useful for estimating the contact angle of other metallic surfaces without knowing a ML value a priori.

physics.chem-ph↗