arXiv subjects

Kenji Yasuoka

Publications and source records attributed to Kenji Yasuoka.

At least 19 recordsLinked to original sources

arXiv2026

Analysis of molecular dynamics simulation data via statistical distances between covariance matrices

Molecular dynamics (MD) simulations are powerful tools for elucidating the macroscopic physical properties of materials from microscopic atomic behaviors. However, the massive, high-dimensional datasets generated by MD simulations pose a significant challenge for analysis, necessitating efficient dimensionality reduction and feature extraction techniques. While existing methods such as principal component analysis and unsupervised learning have been utilized, issues regarding data efficiency and computational cost remain. In this study, we propose a statistical analysis framework focusing on the analysis of the particle data distributions through their covariance matrices, corresponding to the second-order moments of MD trajectory data. Discrepancies between system states are quantified using statistical distances between these covariance matrices. By applying dimensionality reduction to the resulting distance matrix, we extract lower-dimensional features that characterize the systems' dynamics. We validate the proposed method using Lennard-Jones (LJ) particle systems under different temperature conditions, as well as separate bulk systems of ice and liquid water. The results of LJ particles demonstrate an approximately linear correlation between the first principal component obtained through dimensionality reduction of the distance matrix and the diffusion coefficient. This suggests that global physical properties can be effectively inferred from local statistical information, such as covariance matrices, offering a data-efficient alternative for analyzing complex molecular systems. Furthermore, in the case of separate bulk systems of ice and liquid water, the method successfully distinguishes between the two phases, highlighting its potential for characterizing phase transitions and structural differences in molecular systems.

stat.AP↗

arXiv2026

Auto-WHATMD : Automated Wasserstein-based High-dimensional feature extraction Analysis of Trajectories from Molecular Dynamics

Comparing multiple protein systems with variation such as different binding ligands or mutations, and understanding their effects is one of the objectives in molecular dynamics simulations. Representation of these systems by a few features enables quantitative comparison. However, because molecular dynamics simulation trajectories are high-dimensional spatiotemporal data, selection of key features relies on domain expertise, sometimes introducing arbitrary assumptions. Here, we present an approach that uses the optimal transport distance to compare high-dimensional trajectory data, and employs simulated annealing to identify the residues that best distinguish multiple systems. We term this algorithm auto-WHATMD (automated Wasserstein-based High-dimensional feature extraction Analysis for Trajectories of Molecular Dynamics). We applied auto-WHATMD to multiple protein-ligand systems of bromodomain 4 with different ligands, identifying the most discriminative residues in the loop region. Moreover, even a few selected residues were sufficient to capture the correlation with ligand-binding affinities, indicating that auto-WHATMD effectively prioritizes the most informative residues. Our approach can be used to efficiently determine key residues and design features for multiple analogous systems.

physics.chem-ph↗

arXiv2025

Enhanced premelting at the ice-rubber interface using all-atom molecular dynamics simulation

The ice-rubber interface is critical in applications such as tires and shoe outsoles, yet its molecular tribology remains unclear. Using all-atom molecular dynamics simulations, we studied premelting layers at the basal face of ice in contact with styrene-butadiene rubber from 254 to 269 K. Despite its hydrophobicity, rubber enhances structural disorder of interfacial water, promoting premelting. In contrast, water mobility is suppressed by confinement from polymer chains, leading to glassy dynamics distinct from the ice-vapor interface. Near the melting point, rubber chains become more flexible and penetrate the premelting layer, forming a mixed rubber-water region that couples the dynamics of both components. These results suggest that nanoscale roughness and morphology of hydrophobic polymers disrupt ice hydrogen-bond networks, thereby enhancing premelting. Our findings provide molecular-level insight into ice slipperiness and inform the design of polymer materials with controlled ice adhesion and friction.

cond-mat.soft↗

arXiv2025

Navigating Phase Transitions with Path-Finding Algorithms: A Strategic Approach to Replica Exchange Monte Carlo

The replica exchange method is a powerful tool for overcoming slow relaxation in molecular simulations, but its efficiency depends strongly on the choice of the number and interval of replicas and their exchange probabilities. Here, we propose a new optimization scheme based on the Dijkstra algorithm that constructs an optimal exchange path by representing replicas and their exchange probabilities as a graph. Inspired by path-finding techniques widely used in computer science, including applications in game algorithms, our approach ensures that transitions follow a minimum entropy gradient path and effectively speeds up sampling even in systems exhibiting slow relaxation near critical points or phase transition regions. The method provides a systematic way to improve replica exchange efficiency and offers new insights into the control of relaxation dynamics, as demonstrated through applications to the solid-liquid phase transition of the Lennard-Jones bulk system.

cond-mat.stat-mech↗

arXiv2025

Molecular Dynamics Investigation of Static and Dynamic Interfacial Properties in Ice-Polymer Premelting Layers

Premelting at the ice-polymer interfaces, in which a quasi-liquid layer (QLL) forms below the melting point, is strongly influenced by polymer surface chemistry; however, the molecular-scale mechanisms underlying these effects remain poorly understood. This study employs large-scale molecular dynamics simulations combined with machine learning-assisted analysis to elucidate how polymer type (hydrophilic vs hydrophobic) modulates interfacial premelting. Our simulations reveal that hydrophilic and hydrophobic polymer surfaces have distinct effects on the QLL thickness, interfacial water structure, and diffusivity. Specifically, a hydrophilic polymer interface promotes a thicker QLL with more ordered interfacial water and lower diffusivity, whereas a hydrophobic interface induces a thinner QLL with a less ordered interfacial water structure and higher diffusivity. These results advance the understanding of polymer-mediated interfacial melting phenomena and offer guidance for designing anti-icing and low-friction materials.

cond-mat.soft↗

arXiv2024

A Study on Quantum Car-Parrinello Molecular Dynamics with Classical Shadows for Resource Efficient Molecular Simulation

Ab-initio molecular dynamics (AIMD) is a powerful tool to simulate physical movements of molecules for investigating properties of materials. While AIMD is successful in some applications, circumventing its high computational costs is imperative to perform large-scale and long-time simulations. In recent days, near-term quantum computers have attracted much attentions as a possible solution to alleviate the challenge. Specifically, Kuroiwa et al. proposed a new AIMD method called quantum Car-Parrinello molecular dynamics (QCPMD), which exploits the Car-Parrinello method and Langevin formulation to realize cost-efficient simulations at the equilibrium state, using near-term quantum devices. In this work, we build on the proposed QCPMD method and introduce the classical shadow technique to further improve resource efficiency of the simulations. More precisely, classical shadows are used to estimate the forces of all nuclei simultaneously, implying this approach is more effective as the number of molecules increases. We numerically study the performance of our scheme on the $\text{H}_2$ molecule and show that QCPMD with classical shadows can simulate the equilibrium state. Our results will give some insights into efficient AIMD simulations on currently-available quantum computers.

quant-ph↗

arXiv2024

Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly

Amphiphilic molecules spontaneously form self-assembled structures of various shapes depending on their molecular structures, the temperature, and other physical conditions. The functionalities of these structures are dictated by their formations and their properties must be evaluated for reproduction using molecular simulations. However, the assessment of such intricate structures involves many procedural steps. This study investigates the potential of machine-learning models to extract structural features from mesoscale non-ordered self-assembled structures, and suggests a methodology in which machine-learning models for the structural analysis of self-assembled structures are trained on particle types and coordinate data. In the proposed approach, graph neural networks are utilised to extract local structural data for analysis. In simulations using several hundred self-assembled structures of up to 4050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35 % accuracy. As the machine-learning models learn structural characteristics without the need for human-made feature engineering, the proposed method has important potential applications in the field of materials science.

cond-mat.soft↗

arXiv2022

Wetting hysteresis induces effective unidirectional water transport through a fluctuating nanochannel

We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric thermal fluctuations imposed on the channel radius cause unidirectional water flow without osmotic pressure, which can be attributed to hysteresis in the cyclic transition between the wetting/drying states. We show that the water transport depends on fluctuations, such as white, Brownian, and pink noises. Because of the high-frequency components in white noise, fast switching of open and close states inhibits channel wetting. Conversely, pink and Brownian noises generate high-pass filtered net flow. Brownian fluctuation leads to a faster water transport rate, whereas pink noise has a higher capability to overcome osmotic pressure in the opposite direction. A trade-off relationship exists between the resonant frequency of the fluctuation and the flow amplification. The proposed pump can be considered as an analogy for the reversed Carnot cycle, which is the upper limit on the energy conversion efficiency.

cond-mat.soft↗

arXiv2022

A stochastic Hamiltonian formulation applied to dissipative particle dynamics

In this paper, a stochastic Hamiltonian formulation (SHF) is proposed and applied to dissipative particle dynamics (DPD) simulations. As an extension of Hamiltonian dynamics to stochastic dissipative systems, the SHF provides necessary foundations and great convenience for constructing efficient numerical integrators. As a first attempt, we develop the Störmer--Verlet type of schemes based on the SHF, which are structure-preserving for deterministic Hamiltonian systems without external forces, the dissipative forces in DPD. Long-time behaviour of the schemes is shown numerically by studying the damped Kubo oscillator. In particular, the proposed schemes include the conventional Groot-Warren's modified velocity-Verlet method and a modified version of Gibson-Chen-Chynoweth as special cases. The schemes are applied to DPD simulations and analysed numerically.

math.NA↗

arXiv2022

Prediction of transport property via machine learning molecular movements

Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and are used as the ML input. However, to accurately find the input--output patterns, ML requires a sufficiently sized dataset that depends on the complexity of the ML model. Generating such a large dataset from MD simulations is not ideal because of their high computation cost. In this study, we present a simple supervised ML method to predict the transport properties of materials. To simplify the model, an unsupervised ML method obtains an efficient representation of molecular movements. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. Furthermore, simplicity facilitates the interpretation of the model to understand the molecular mechanics of viscosity. We revealed two types of molecular mechanisms that contribute to low viscosity.

physics.chem-ph↗

arXiv2022

Correlation between ordering and shear thinning in confined liquids

Despite the extensive research that has been conducted for decades on the behavior of confined liquids, detailed knowledge of this phenomenon, particularly in the mixed/boundary lubrication regime, remains limited. This can be attributed to several factors including the difficulty of direct experimental observations of the behavior of lubricant molecules under non-equilibrium conditions, the high computational cost of molecular simulations to reach steady state, and the low signal-to-noise ratio at extremely low shear rates corresponding to actual operating conditions. To this end, we studied the correlation between the structure formation and shear viscosity of octamethylcyclotetrasiloxane confined between two mica surfaces in a mixed/boundary lubrication regime. Three different surface separations corresponding to two-, three-, and five-layered structures were considered to analyze the effect of confinement. The orientational distributions with one specific peak for $n=2$ and two distributions, including a parallel orientation with the surface normal for $n>2$, were observed at rest. The confined liquids exhibited a distinct shear-thinning behavior independent of surface separations for a relatively low sliding velocity, $V_{\rm x}\lesssim 10^{-1}\,{\rm m/s}$. However, the shear viscosities at $V_{\rm x}\lesssim 10^{-1}\,{\rm m/s}$ depended on the number of layered structures. Newtonian behavior was observed with a further increase in the sliding velocity. Furthermore, we found a strong correlation between the degree of molecular orientation and the shear viscosity of the confined liquids. The magnitude of the shear viscosity of the confined liquids can primarily be determined by the degree of molecular orientation, and shear-thinning originates from the vanishing of specific orientational distributions with increasing sliding velocity.

cond-mat.soft↗

arXiv2022

Natural quantum reservoir computing for temporal information processing

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

quant-ph↗

arXiv2022

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data

MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. Therefore, we investigated the effectiveness of adding information from other particles to the learning process. In the experiment of the polyethylene system, when the dynamics of three particles of each molecule were used, the diffusion was successfully predicted using one-third of the time length of the training data, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method.

physics.chem-ph↗

arXiv2022

Impact of Free Energy of Polymers on Polymorphism of Polymer-Grafted Nanoparticles

We focus on polymer-grafted nanoparticles (PGNP). A PGNP is composed of two different layers: the hard core of a nanoparticle and the soft corona of grafted polymers on the surface. It is predicted that PGNPs with these two distinct layers will have similar behaviors as star polymers and hard spheres. The interaction between PGNPs strongly depend upon their grafting density and the length of the grafted polymer chains, N. Thus, PGNP may exhibit polymorphism. Moreover, it is expected that crystals made from PGNPs will be structurally tough due to the entanglement of grafted polymers. The crystal polymorph of PGNP is explored using molecular dynamics simulations. We succeeded in finding FCC/HCP and BCC crystals depending on the length of the grafted polymer chain. When N is small, PGNPs behave like hard spheres. The crystals formed are arranged in FCC/HCP structure, much like the phase transition observed in an Alder transition. When N is large enough, the increase in the free energy of grafted polymers can no longer be neglected. Thus, the crystals formed in these systems are arranged in BCC structure, which has a lower density than FCC/HCP. When N is not too small or large, FCC/HCP structures are observed when the concentration of PGNPs is low, but a phase transition occurs when the concentration of PGNPs becomes higher with the compression of the system. Again, the increase in free energy of grafted polymers can no longer be neglected and the crystals arrange themselves in a BCC structure. Also, it can be revealed that the lattice spacing of PGNP crystals can be controlled easily and widely by the chain length. These results should play an important role in many future simulations and experimental studies of PGNP crystals.

cond-mat.soft↗

arXiv2021

Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach

Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by the binding ligand. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a novel method that represents protein behavioral change upon ligand binding with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension-reduction method extracts a dynamic feature that is strongly correlated to the binding affinities. Moreover, the residues that play important roles in protein-ligand interactions are specified based on their contribution to the differences. These results indicate the potential for dynamics-based drug discovery.

q-bio.BM↗

arXiv2019

Dataflow programming for the analysis of molecular dynamics with AViS, an analysis and visualization software application

The study of molecular dynamics simulations is largely facilitated by analysis and visualization toolsets. However, these toolsets are often designed for specific use cases and those only, while scripting extensions to such toolsets is often exceedingly complicated. To overcome this problem, we designed a software application called AViS which focuses on the extensibility of analysis. By utilizing the dataflow programming (DFP) paradigm, algorithms can be defined by execution graphs, and arbitrary data can be transferred between nodes using visual connectors. Extension nodes can be implemented in either Python, C++, and Fortran, and combined in the same algorithm. AViS offers a comprehensive collection of nodes for sophisticated visualization state modifications, thus greatly simplifying the rules for writing extensions. Input files can also be read from the server automatically, and data is fetched automatically to improve memory usage. In addition, the visualization system of AViS uses physically-based rendering techniques, improving the 3D perception of molecular structures for interactive visualization. By performing two case studies on complex molecular systems, we show that the DFP workflow offers a much higher level of flexibility and extensibility when compared to legacy workflows. The software source code and binaries for Windows, MacOS, and Linux are freely available at https://avis-md.github.io/.

cs.CE↗

arXiv2019

Molecular Flow Monte Carlo

In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid extremely high energy configurations while also retaining the ability to quickly generate new and independent trial states. Thus, we introduce a continuous normalizing flow method which can quickly generate independent states for various proposal distributions using a first-order differential equation. We define this continuous normalizing molecular flow approach based on two-body intermolecular interactions to achieve a probability distribution transformation method which yields distributions which have probability densities of zero when molecule pairs are in close proximity; while in all other cases, the probability density is compressed such that it is spatial uniform. This transform provides the proposal distribution which generates no states of extremely high energy. We find that an inverse square flow is applicable as the continuous normalizing molecular flow. Using the transformed distribution, we can perform the Metropolis-Hastings method more efficiently. The high efficiency of the proposed method is demonstrated using simple molecular systems.

physics.comp-ph↗

arXiv2014

Effects of the Cu off-stoichiometry on transport properties of wide gap p-type semiconductor, layered oxysulfide LaCuSO

Layered oxysulfide LaCuSO (x = 0-0.03) was prepared to elucidate the effect of Cu off-stoichiometry on their electrical and thermal transport properties. Electrical resistivity drastically decreases down from ~10^5 ohmcm to ~10^-1 ohmcm as a result of Cu deficiency (x = 0.01) at 300 K. Thermal conductivity of the samples at 300 K, which is dominated by lattice components, is estimated to be 2.3(3) Wm^-1K^-1. Stoichiometric LaCuSO has an optical band gap of 3.1 eV, while broad optical absorption at photon energies of approximately 2.1 eV was observed for Cu-deficient samples. Density functional theory calculation suggests that these broad absorption structures probably originate from the in-gap states generated by the sulfur vacancies created to compensate the charge imbalance due to Cu off-stoichiometry. These results clearly demonstrate that Cu deficiency plays a crucial role in determining the electrical transport properties of Cu-based p-type transparent semiconductors.

cond-mat.mtrl-sci↗