arXiv subjects

Kousuke Nakano

Publications and source records attributed to Kousuke Nakano.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2022

High-pressure BaCN$_2$ phases explored by genetic algorithm

Polymers containing nitrogen have attracted much attention in connection with their application to high energy density materials (HEDMs), in which energy is inherent in the triple bond. It is an interesting question whether such polymerized phases appear in the high-pressure phase of metal carbodiimide MCN$_2$, of which synthesis have been reported in recent years, but few studies have investigated the crystal structure at high pressure. We have adopted a structure search based on the genetic algorithm coupled with ab initio electronic structure calculations to investigate possible crystal structures that may appear in the high-pressure phase of BaCN$_2$. The structure search successfully reproduced the previously reported crystal structures in the lower pressure range. With confirmed reliability of its predictive ability, the genetic search further predicts a polymerized phase with Ima2 appearing at higher pressure above 42 GPa. The polymerized phase takes the structure of a linear network of CN$_3$ planar triangular units. It is understood that the anion site units CN$_2$, which are close to each other under high pressure, form covalent bonds directly with each other and stabilize the phase.

cond-mat.mtrl-sci↗

arXiv2022

High pressure hydrogen by machine learning and quantum Monte Carlo

We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use kernel regression in combination with SOAP (Smooth Overlap of Atomic Position) features, implemented here in a very efficient way. The key ingredients are: i) a sparsification technique, based on farthest point sampling, ensuring generality and transferability of our MLPs and ii) the so called $Δ$-learning, allowing a small training data set, a fundamental property for highly accurate but computationally demanding calculations, such as the ones based on quantum Monte Carlo. As the first application we present a benchmark study of the liquid-liquid transition of high-pressure hydrogen and show the quality of our MLP, by emphasizing the importance of high accuracy for this very debated subject, where experiments are difficult in the lab, and theory is still far from being conclusive.

cond-mat.str-el↗

arXiv2021

Application of canonical augmentation to the atomic substitution problem

A common approach for studying a solid solution or disordered system within a periodic ab-initio framework is to create a supercell in which a certain amount of target elements is substituted with other ones. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from the large number of substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. A straightforward solution would be to classify them after determining all possible patterns, but it is redundant and practically unfeasible. Therefore, to alleviate this drawback, we developed a new formalism based on the {\it canonical augmentation}, and successfully applied it to the atomic substitution problem. Our developed \verb|python| software package, which is called \textsc{SHRY} (\underline{S}uite for \underline{H}igh-th\underline{r}oughput generation of models with atomic substitutions implemented by p\underline{y}thon), enables us to pick up only symmetry-inequivalent structures from the vast number of candidates very efficiently. We demonstrate that the computational time required by our algorithm to find $N$ symmetry-inequivalent structures scales {\it linearly} with $N$ up to $\sim 10^9$. This is the best scaling for such problems.

cond-mat.mtrl-sci↗

arXiv2021

Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs

Although Boron nitride (BN) is a well-known compound widely used for engineering and scientific purposes, the phase stability of its polymorphs, one of its most fundamental properties, is still under debate. The ab initio determination of the ground state of the BN polymorphs, such as hexagonal and zinc-blende, is difficult because of the elusive Van der Waals interaction, which plays a decisive role in some of the polymorphs, making quantitative prediction highly challenging. Hence, despite multiple theoretical studies, there has been no consensus on the ground state yet, primarily due to contradicting reports. In this study, we apply a state-of-the-art ab initio framework - fixed-node diffusion Monte Carlo (FNDMC), to four well known BN polymorphs, namely hexagonal, rhombohedral, wurtzite, and zinc-blende BNs. Our FNDMC calculations show that hBN is thermodynamically the most stable among the four polymorphs at 0 K as well as at 300K. This result agrees with the experimental data of Corrigan~{\it et al.} and Fukunaga. The conclusions are consistent with those obtained using other high-level methods, such as coupled cluster. We demonstrate that the FNDMC is a powerful method to address polymorphs that exhibit bonds of various forms. It also provides valuable information, like reliable reference energies, when reliable experimental data are missing or difficult to access. Our findings should promote the application of FNDMC for other van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2021

Space-warp coordinate transformation for efficient ionic force calculations in quantum Monte Carlo

Ab-initio quantum Monte Carlo (QMC) methods are a state-of-the-art computational approach to obtaining highly accurate many-body wave functions. Although QMC methods are widely used in physics and chemistry to compute ground-state energies, calculation of atomic forces is still under technical/algorithmic development. Very recently, force evaluation has started to become of paramount importance for the generation of machine-learning force-field potentials. Nevertheless, there is no consensus regarding whether an efficient algorithm is available for the QMC force evaluation, namely one that scales well with the number of electrons and the atomic numbers. In this study, we benchmark the accuracy of all-electron variational Monte Carlo (VMC) and lattice-regularized diffusion Monte Carlo (LRDMC) forces for various mono- and heteronuclear dimers. The VMC and LRDMC forces were calculated with and without the so-called space-warp coordinate transformation (SWCT) and appropriate regularization techniques to remove the infinite variance problem. The LRDMC forces were computed with the Reynolds (RE) and variational-drift (VD) approximations. The potential energy surfaces obtained from the LRDMC energies give equilibrium bond lengths ($r_{\rm eq}$) and harmonic frequencies ($ω$) very close to the experimental values for all dimers, improving the corresponding VMC results. The LRDMC forces improve the VMC forces, implying that it is worth computing the DMC forces beyond VMC in spite of the higher computational cost. We find that the ratio of computational costs between QMC energy and forces scales as $Z^{\sim 2.5}$ without the SWCT. In contrast, the application of the SWCT makes the ratio {\it independent} of $Z$. As such, the accessible QMC system size is not affected by the evaluation of ionic forces but governed by the same scaling as the total energy one.

physics.chem-ph↗

arXiv2021

High-pressure Mg-Sc-H phase diagram and its superconductivity from first-principles calculations

In this work, global search for crystal structures of ternary Mg-Sc-H hydrides (Mg$_x$Sc$_y$H$_z$) under high pressure ($100 \le P \le 200$ GPa) were performed using the evolutionary algorithm and first-principles calculations. Based on them, we computed the thermodynamic convex hull and pressure-dependent phase diagram of Mg$_x$Sc$_y$H$_z$ for $z/(x+y) < 4$. We have identified the stable crystal structures of four thermodynamically stable compounds with the higher hydrogen content, i.e., $R\bar{3}m$-MgScH$_{6}$, $C2/m$-Mg$_{2}$ScH$_{10}$, $Immm$-MgSc$_{2}$H$_{9}$ and $Pm\bar{3}m$-Mg(ScH$_{4}$)$_{3}$. Their superconducting transition temperatures were computationally predicted by the McMillan-Allen-Dynes formula combined with first-principles phonon calculations. They were found to exhibit superconductivity; among them, $R\bar{3}m$-MgScH$_{6}$ was predicted to have the highest $T_{c}$ (i.e. 23.34 K) at 200 GPa.

cond-mat.supr-con↗

arXiv2021

The systematic study on the stability and superconductivity of Y-Mg-H compounds under high pressure

Motivated by recent discovery of yttrium-based high-temperature ternary superconducting hydrides (e.g., CaYH$_{12}$, LaYH$_{12}$, and ScYH$_{6}$), we have employed evolutionary algorithm and first-principles calculations to comprehensively examine the structural stability and superconductivity of the YMgH$_{x}$ system at high pressure. The hydrogen content $x$ and the pressure are both important factors in the stability of these candidate structures. We find that the stability of hydrogen-rich materials frequently necessitates higher pressure. For instance, the pressures to stabilize $P4/mmm$-YMgH$_{8}$ and $Cmmm$-YMgH$_{12}$ are both more than 250 GPa. Hydrogen-less materials, such as $I4_{1}/amd$-YMgH$_{2}$ and $P6_{3}/mmc$-YMgH$_{3}$, can be stable at pressures as low as 100 GPa. In addition, we find a metastable structure for YMgH$_{6}$ with the same space group as the $P4/mmm$-YMgH$_{8}$. A metastable sodalite-like face-centered cubic (FCC) structure is also found in YMgH$_{12}$. These four clathrate structures of $P4/mmm$-YMgH$_{6}$, $P4/mmm$-YMgH$_{8}$, $Cmmm$-YMgH$_{12}$, and $Fd\bar{3}m$-YMgH$_{12}$ is made up of H14, H18, H24, and H24 cages, respectively, in which the H-H pair exhibits weak covalent bonding. According to phonon calculations, $P4/mmm$-YMgH$_{6}$ and $P4/mmm$-YMgH$_{8}$ require a pressure of 300 GPa to maintain dynamic stability, however $Cmmm$-YMgH$_{12}$ and $Fd\bar{3}m$-YMgH$_{12}$ can maintain dynamic stability at pressures of 200 GPa and 250 GPa, respectively. Electron-phonon coupling calculations indicate that they might be potential high-temperature superconductors, with superconductivity intimately linked to the H cage structure. The sodalite structure $Fd\bar{3}m$-YMgH$_{12}$ has a $T_\mathrm{c}$ value of 190 K and a strong electron-phonon coupling constant of 2.18.

cond-mat.supr-con↗

arXiv2021

High-$T_c$ superconducting hydrides formed by LaH$_{24}$ and YH$_{24}$ cage structures as basic blocks

Based on recent studies regarding high-temperature (high-$T_c$) La-Y ternary hydrides (e.g., $P{\bar{1}}$-La$_2$YH$_{12}$, $Pm{\bar{3}}m$-LaYH$_{12}$, and $Pm{\bar{3}}m$-(La,Y)H$_{10}$ with a maximum $T_c \sim 253$ K), we examined the phase and structural stabilities of the (LaH$_6$)(YH$_6$)$_y$ series as high-$T_c$ ternary hydride compositions using a genetic algorithm and $\it ab$ $\it initio$ calculations. Our evaluation showed that the $Pm\bar{3}m$-LaYH$_{12}$ reported in the previous study was unstable during decomposition into $R\bar{3}c$-LaH$_{6}$ + $Im\bar{3}m$-YH$_{6}$. We also discovered new crystal structures, namely $Cmmm$-LaYH$_{12}$ ($y=1$), $R\bar{3}c$-LaYH$_{12}$ ($y=1$), $Cmmm$-LaY$_3$H$_{24}$ ($y=3$), and $R\bar{3}$-LaY$_3$H$_{24}$ ($y=3$), showing stability against such decomposition. While $R\bar{3}c$ ($y=1$) and $R\bar{3}$ ($y=3$) did not exhibit superconductivity owing to the extremely low density of states at the Fermi level, $Cmmm$ phases exhibited a $T_{c}$ of approximately 140~K at around 200~GPa owing to the extremely high electron--phonon coupling constant ($λ$ = 1.876 for LaYH$_{12}$). By the twice longer stacking for $Cmmm$-LaY$_3$H$_{24}$, the coupling constant increased owing to the chemical pressure of Y, leading to a slightly increased $T_{c}$.

cond-mat.supr-con↗

arXiv2021

Atomic forces by quantum Monte Carlo: application to phonon dispersion calculation

We report the first successful application of the {\it ab initio} quantum Monte Carlo (QMC) framework to a phonon dispersion calculation. A full phonon dispersion of diamond is successfully calculated at the variational Monte Carlo (VMC) level, based on the frozen-phonon technique. The VMC-phonon dispersion is in good agreement with the experimental results, giving renormalized harmonic optical frequencies very close to the experimental values, by significantly improving upon density functional theory (DFT) in the generalized gradient approximation. Key to success for the QMC approach is the statistical error reduction in atomic force evaluation. We show that this can be achieved by using well conditioned atomic basis sets, by explicitly removing the basis-set redundancy, which reduces the statistical error of forces by up to two orders of magnitude. This leads to affordable and accurate QMC-phonons calculations, up to $10^{4}$ times more efficient than previous attempts, and paves the way to new applications, particularly in correlated materials, where phonons have been poorly reproduced so far.

cond-mat.mtrl-sci↗

arXiv2021

Diffusion Monte Carlo evaluation of disiloxane linearization barrier

The disiloxane molecule is a prime example of silicate compounds containing the Si-O-Si bridge. The molecule is of significant interest within the field of quantum chemistry, owing to the difficulty in theoretically predicting its properties. Herein, the linearisation barrier of disiloxane is investigated using a fixed-node diffusion Monte Carlo (FNDMC) approach, which is currently the most reliable {\it ab initio} method in accounting for an electronic correlation. Calculations utilizing the density functional theory (DFT) and the coupled cluster method with single and double substitutions, including noniterative triples (CCSD(T))are carried out alongside FNDMC for comparison. Two families of basis sets are used to investigate the disiloxane linearisation barrier - Dunning's correlation-consistent basis sets cc-pV$x$Z ($x = $ D, T, and Q) and their core-valence correlated counterparts, cc-pCV$x$Z. It is concluded that FNDMC successfully predicts the disiloxane linearisation barrier and does not depend on the completeness of the basis sets as much as DFT or CCSD(T), thus establishing its suitability.

physics.chem-ph↗

arXiv2021

High-$T_c$ ternary metal hydrides, YKH$_{12}$ and LaKH$_{12}$, discovered by machine learning

The search for hydride compounds that exhibit high $T_c$ superconductivity has been extensively studied. Within the range of binary hydride compounds, the studies have been developed well including data-driven searches as a topic of interest. Toward the search for the ternary systems, the number of possible combinations grows rapidly, and hence the power of data-driven search gets more prominent. In this study, we constructed various regression models to predict $T_c$ for ternary hydride compounds and found the extreme gradient boosting (XGBoost) regression giving the best performance. The best performed regression predicts new promising candidates realizing higher $T_c$, for which we further identified their possible crystal structures. Confirming their lattice and thermodynamical stabilities, we finally predicted new ternary hydride superconductors, YKH$_{12}$ [$C2/m$ (No.12), $T_c$=143.2 K at 240 GPa] and LaKH$_{12}$ [$R\bar{3}m$ (No.166), $T_c$=99.2 K at 140 GPa] from first principles.

cond-mat.supr-con↗

arXiv2020

Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to $\sim$ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order.

physics.comp-ph↗

arXiv2020

General correlated geminal ansatz for electronic structure calculations: exploiting Pfaffians in place of determinants

We propose here a single Pfaffian correlated variational ansatz, that dramatically improves the accuracy with respect to the single determinant one, while remaining at a similar computational cost. A much larger correlation energy is indeed determined by the most general two electron pairing function, including both singlet and triplet channels, combined with a many-body Jastrow factor, including all possible spin-spin spin-density and density-density terms. The main technical ingredient to exploit this accuracy is the use of the Pfaffian for antisymmetrizing an highly correlated pairing function, thus recovering the Fermi statistics for electrons with an affordable computational cost. Moreover the application of the Diffusion Monte Carlo, within the fixed node approximation, allows us to obtain very accurate binding energies for the first preliminary calculations reported in this study: C$_2$, N$_2$ and O$_2$ and the benzene molecule. This is promising and remarkable, considering that they represent extremely difficult molecules even for computationally demanding multi-determinant approaches, and opens therefore the way for realistic and accurate electronic simulations with an algorithm scaling at most as the fourth power of the number of electrons.

physics.chem-ph↗

arXiv2020

Quantum annealing approach to Ionic Diffusion in Solid

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ${ab\ initio}$ techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors.We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods.

quant-ph↗

arXiv2020

TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo

TurboRVB is a computational package for {\it ab initio} Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC), and Diffusion Monte Carlo in its robust and efficient lattice regularized variant. A key feature of the code is the possibility of using strongly correlated many-body wave functions. The electronic wave function (WF) is obtained by applying a Jastrow factor, which takes into account dynamical correlations, to the most general mean-field ground state, written either as an antisymmetrized geminal product with spin-singlet pairing, or as a Pfaffian, including both singlet and triplet correlations. This wave function can be viewed as an efficient implementation of the so-called resonating valence bond (RVB) ansatz, first proposed by L. Pauling and P. W. Anderson in quantum chemistry and condensed matter physics, respectively. The RVB ansatz implemented in TurboRVB has a large variational freedom, including the Jastrow correlated Slater determinant as its simplest, but nontrivial case. Moreover, it has the remarkable advantage of remaining with an affordable computational cost, proportional to the one spent for the evaluation of a single Slater determinant. The code implements the adjoint algorithmic differentiation that enables a very efficient evaluation of energy derivatives, comprising the ionic forces. Thus, one can perform structural optimizations and molecular dynamics in the canonical NVT ensemble at the VMC level. For the electronic part, a full WF optimization is made possible thanks to state-of-the-art stochastic algorithms for energy minimization. The code has been efficiently parallelized by using a hybrid MPI-OpenMP protocol, that is also an ideal environment for exploiting the computational power of modern GPU accelerators.

physics.comp-ph↗

arXiv2020

Feature space of XRD patterns constructed by auto-encorder

It would be a natural expectation that only major peaks, not all of them, would make an important contribution to the characterization of the XRD pattern. We developed a scheme that can identify which peaks are relavant to what extent by using auto-encoder technique to construct a feature space for the XRD peak patterns. Individual XRD patterns are projected onto a single point in the two-dimensional feature space constructed using the method. If the point is significantly shifted when a peak of interest is masked, then we can say the peak is relevant for the characterization represented by the point on the space. In this way, we can formulate the relevancy quantitatively. By using this scheme, we actually found such a peak with a significant peak intensity but low relevancy in the characterization of the structure. The peak is not easily explained by the physical viewpoint such as the higher-order peaks from the same plane index, being a heuristic finding by the power of machine-learning.

physics.comp-ph↗

arXiv2019

Speeding up the ab initio diffusion Monte Carlo by a smart lattice regularization

One of the most significant drawbacks of the all-electron ab initio diffusion Monte Carlo (DMC) is that its computational cost drastically increases with the atomic number ($Z$), which typically scales with $Z^{\sim 6}$. In this study, we introduce an algorithm based on a very efficient implementation of the Lattice Regularized Diffusion Monte Carlo (LRDMC), where the conventional time discretization is replaced by its lattice space counterpart. This scheme enables us to conveniently adopt a small lattice space in the vicinity of nuclei, and a large one in the valence region, by which a considerable speedup is achieved, especially for large atomic number $Z$. Indeed, the computational performances of our algorithm can be theoretically established by using the Thomas-Fermi model for heavy atoms, yielding an almost affordable scaling with the atomic number, i.e., $Z^{\sim 5}$. This opens the way for efficient and accurate all-electron ab initio DMC in electronic structure calculations.

physics.comp-ph↗