arXiv subjects

Nobuyuki Matubayasi

Publications and source records attributed to Nobuyuki Matubayasi.

At least 19 recordsLinked to original sources

arXiv2026

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates

In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems.

physics.chem-ph↗

arXiv2026

Machine learning evaluation of structural descriptors for supercooled water

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these properties, motivating structural descriptors that characterize local molecular environments. These structural descriptors quantify features such as tetrahedral order, local density, and the separation between the first and second coordination shells; however, they have largely been proposed independently, with limited systematic comparison. Here we evaluate 16 previously proposed descriptors using a neural-network-based temperature classification framework, enabling an objective assessment of their ability to distinguish temperature-dependent structural changes in supercooled water. We further apply an explainable artificial intelligence method that identifies the structural features responsible for the model predictions. This approach reveals how different descriptors encode local structural information and establishes a data-driven framework for benchmarking structural descriptors in liquid water.

cond-mat.soft↗

arXiv2026

Classification of interfacial water governed by water-polymer interactions in hydrated polymers: A molecular dynamics simulation study of ethylene-based and acrylate polymers

We perform molecular dynamics simulations to investigate hydration structures and dynamics in seven water-containing polymers: PVA, PHEA, PHEMA, PBA, PMEMA, PEG, and PMEA. The analysis integrates four perspectives: the water-content dependence of the glass transition temperature $T_g$, polymer chain fluctuations characterized by dihedral angle distributions, hydrogen-bond lifetimes $τ_{\mathrm{HB}}$ between water and polymer functional groups, and the localization and exchange dynamics of confined water quantified by the distinct part of van Hove correlation function. Hydroxyl-containing polymers (PVA, PHEA, and PHEMA) exhibit relatively high dry-state $T_g$ values and its pronounced depression upon hydration. Chain fluctuations are limited, and $τ_{\mathrm{HB}}$ follows Arrhenius behavior, forming localized hydration shells. In contrast, PMEMA and PBA show low equilibrium water contents and hydrophobic character; although their dry-state $T_g$ values are moderately lower and less sensitive to water content, chain fluctuations remain small, and $τ_{\mathrm{HB}}$ also obeys Arrhenius behavior, with hydrophobic aggregation promoting water localization. PEG and PMEA display low dry-state $T_g$ values and weak water-content dependence. Greater rotational freedom around ether or methoxy oxygen atoms leads to larger chain fluctuations and loosely bound water. Below $T_g$, $τ_{\mathrm{HB}}$ between water and ether or methoxy oxygen atoms exhibits super-Arrhenius behavior. These results clarify three hydration types: highly hydrated (PVA, PHEA, and PHEMA), hydrophobic (PMEMA and PBA), and flexibly hydrated (PEG and PMEA), and provide a molecular-level framework for interpreting interfacial water governed by water-polymer interactions.

cond-mat.soft↗

arXiv2026

IEPDYN: Integral-equation formalism of population dynamics

We propose the integral-equation formalism of population dynamics (IEPDYN) to describe the population dynamics of distinct configurational states. According to classical reaction dynamics theory, the probability density associated with a given state obeys the Liouville equation, including influx from and efflux to neighboring states. By introducing a Markov approximation for the crossing of boundaries separating the states, tractable integral equations governing the state populations are derived. Once the time-dependent quantities appearing in these equations are evaluated, the population dynamics on long timescales can be obtained. Because these quantities depend only on a few states in the local neighborhood of a given state, they can be computed using a set of short-timescale molecular dynamics (MD) simulations. The IEPDYN method is formulated in continuous time and therefore does not rely on a coarse-grained timescale (lag time). Consequently, kinetic quantities obtained from IEPDYN are free from lag-time dependence, which has been discussed as a limitation in other approaches. We apply the IEPDYN method to the binding and unbinding kinetics of CH$_4$/CH$_4$, Na$^+$/Cl$^-$, and 18-crown-6-ether (crown ether)/K$^+$ in water. For both kinetics, the time constants estimated from the IEPDYN method are almost comparable to those obtained from brute-force MD simulations. The required timescale of each MD trajectory in the IEPDYN method is approximately two orders of magnitude shorter than that in the brute-force MD approach in the crown ether/K$^+$ system. This reduction in the trajectory timescale enables applications to complex binding and unbinding systems whose characteristic timescales are far beyond those directly accessible by brute-force MD simulations.

physics.chem-ph↗

arXiv2026

Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions

The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the two associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry functions (ACSFs), which serve as input variables for the neural network. In addition, an explainable artificial intelligence technique is applied to identify ACSFs that contribute to the reaction coordinate. A comparative analysis of their correlation with CVs representing water bridging structures, such as interionic water density and the number of water molecules coordinating both ions, further provides a molecular-level interpretation of the ion association-dissociation mechanism in water.

physics.chem-ph↗

arXiv2025

Elucidation of the Correlation between Molecular Conformation and Shear Viscosity of Polymer Melts under Steady-State Shear Flow

The rheological behavior of polymer melts is strongly influenced by parameters such as chain length, chain stiffness, and architecture. In particular, shear thinning, characterized by a power-law decrease in shear viscosity with increasing shear rate, has been widely investigated through molecular dynamics simulations. A central question is the connection between molecular conformation under steady flow and the resulting shear-thinning response. In this study, we employ coarse-grained molecular dynamics simulations of linear and ring polymers with varying chain stiffness to examine this relationship, with chain conformations quantified by the gyration tensor. We identified a strong correlation between the velocity-gradient direction component of the gyration tensor and shear viscosity, which exhibits a clear scaling relationship. This indicates that chain extension along the velocity-gradient direction governs the effective frictional force. Notably, this behavior emerges as a general feature, independent of chain architecture and chain stiffness. In addition, shear viscosity was found to correlate with the component of the gyration tensor corresponding to the direction that is not directly influenced by advective effects of shear flow. Because advection is absent in the direction, polymer chains can be regarded as diffusing freely, and the extent of this diffusion appears to be controlled by the shear viscosity.

cond-mat.soft↗

arXiv2025

Dual effect of cholesterol on interfacial water dynamics in lipid membranes: Interplay between membrane packing and hydration

Water contained within biological membranes plays a critical role in maintaining the separation between intracellular and extracellular environments and facilitating biochemical processes. Variations in membrane composition and temperature lead to phase state changes in lipid membranes, which in turn influence the structure and dynamics of the associated interfacial water. In this study, molecular dynamics simulations were performed on binary membranes composed of dipalmitoylphosphatidylcholine (DPPC) or palmitoyl sphingomyelin (PSM) mixed with cholesterol (Chol). To elucidate the effects of Chol on interfacial water, we examined the orientation and hydrogen-bonding behavior of water molecules spanning from the membrane interior to the interface. As the Chol concentration increased, a transient slow down in water dynamics was observed in the ripple phase at 303 K. Conversely, at higher Chol concentrations, water dynamics were accelerated relative to pure lipid membranes across all temperatures studied. Specifically, at a Chol concentration of 50%, the hydrogen bond lifetime in DPPC membranes decreased to approximately 0.5-0.7 times that of pure lipid membranes. This nonmonotonic behavior is attributed to the combined effects of membrane packing induced by Chol and a reduced density of lipid molecules in the hydrophilic region, offering key insights for modulating the dynamical properties of interfacial water.

cond-mat.soft↗

arXiv2025

Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism

Glass-forming liquids exhibit slow dynamics below their melting temperatures, maintaining an amorphous structure reminiscent of normal liquids. Distinguishing microscopic structures in the supercooled and high-temperature regimes remains a debated topic. Building on recent advances in machine learning, particularly Graph Neural Networks (GNNs), our study automatically extracts features, unveiling fundamental mechanisms driving structural changes at varying temperatures. We employ the self-attention mechanism to generate attention coefficients that quantify the importance of connections between graph nodes, providing insights into the rationale behind GNN predictions. Exploring structural changes with decreasing temperature through the GNN+self-attention using physically-defined structural descriptors, including the bond-orientational order parameter, Voronoi cell volume, and coordination number, we identify strong correlations between high attention coefficients and more disordered structures as a key indicator of variations in glass-forming liquids.

cond-mat.soft↗

arXiv2025

Characterizing the embedded states of a fluorescent probe within a lipid bilayer using molecular dynamics simulations

The physicochemical properties of lipid bilayers (membranes) are closely associated with various cellular functions and are often evaluated using absorption and fluorescence spectroscopies. For instance, by employing fluorescent probes that exhibit spectra reflective of the surrounding membrane environment, one can estimate the membrane polarity. Thus, elucidating how such probes are embedded within the membranes would be beneficial for enabling a deeper interpretation of the spectra. Here, we apply molecular dynamics (MD) simulation with an enhanced sampling method to investigate the embedded state of 6-propionyl-2-dimethylaminonaphthalene (Prodan) within a membrane composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), as well as its variation upon the addition of ethanol as a cosolvent to the aqueous phase. In the absence of ethanol, it is found that the bulky moieties of Prodan (propionyl and dimethylamine groups) prefer to be oriented toward the membrane center owing to the voids existing near the center. The structural change in the membrane induced by the addition of ethanol causes a reduction in the void population near the center, resulting in a diminished orientation preference of Prodan.

cond-mat.soft↗

arXiv2025

Investigating the hyperparameter space of deep neural network models for reaction coordinates

Identifying reaction coordinates (RCs) is a key to understanding the mechanism of reactions in complex systems. Deep neural network (DNN) and machine learning approaches have become a powerful tool to find the RC. On the other hand, the hyperparameters that determine the DNN model structure can be highly flexible and are often selected intuitively and in a non-trivial and tedious manner. Furthermore, how the hyperparameter choice affects the RC quality remains obscure. Here, we explore the hyperparameter space by developing the hyperparameter tuning approach for the DNN model for RC and investigate how the parameter set affects the RC quality. The DNN model is built to predict the committor along the RC from various collective variables by minimizing the cross-entropy function; the hyperparameters are automatically determined using the Bayesian optimization method. The approach is applied to study the isomerization of alanine dipeptide in vacuum and in water, and the features that characterize the RC are extracted using the explainable AI (XAI) tools. The results show that the DNN models with diverse structures can describe the RC with similar accuracy, and furthermore, the features analyzed by XAI are highly similar. This indicates that the hyperparameter space is multimodal. The electrostatic potential from the solvent to the hydrogen H18 plays an important role in the RC in water. The current study shows that the structure of the DNN models can be rather flexible, while the suitably optimized models share the same features; therefore, a common mechanism from the RC can be extracted.

physics.chem-ph↗

arXiv2024

Flexible framework of computing binding free energy using the energy representation theory of solution

Host-guest binding plays a crucial role in the functionality of various systems, and its efficiency is often quantified using the binding free energy, which represents the free-energy difference between the bound and dissociated states. Here, we propose a methodology to compute the binding free energy based on the energy representation (ER) theory of solution that enables us to evaluate the free-energy difference between the systems of interest with the molecular dynamics (MD) simulations. Unlike the other free-energy methods, such as the Bennett acceptance ratio (BAR), the ER theory does not require the MD simulations for hypothetical intermediate states connecting the systems of interest, leading to reduced computational costs. By constructing the thermodynamic cycle of the binding process that is suitable for the ER theory, a robust calculation of the binding free energy is realized. We apply the present method to the self-association of \textit{N}-methylacetamide (NMA) in different solvents and the binding of aspirin to $β$-cyclodextrin (CD) in water. In the former case, the present method estimates that the binding free energy decreases as the solvent polarity decreases. This trend is consistent with the experimental finding. For the latter system, the binding free energies for the two representative CD-aspirin bound complexes, primary (P) and secondary (S) complexes, are estimated to be $-5.2\pm 0.1$ and $-5.03\pm 0.09~\mathrm{kcalmol^{-1}}$, respectively. These values are satisfactorily close to those from the BAR method [$-4.2\pm 0.2$ and $-4.1\pm 0.2\mathrm{kcal~mol^{-1}}$ for P and S, respectively]. Furthermore, the interaction-energy component analysis reveals that the van der Waals interaction between aspirin and CD dominantly contributes to the stabilization of the bound complexes, that is in harmony with the well-known binding mechanism in the CD systems.

physics.chem-ph↗

arXiv2024

Atomistic analysis of nematic phase transition in 4-cyano-4$^{\prime}$-$n$-alkyl biphenyl liquid crystals: Sampling for the first-order phase transition and the free-energy decomposition

Molecular dynamics (MD) simulations were conducted using the generalized replica exchange method (gREM) on the 4-cyano-4$^{\prime}$-$n$-alkylbiphenyl ($n$CB) system with $n=5$, 6, 7, and 8, which exhibits a nematic-isotropic (NI) phase transition. Sampling near the phase transition temperature in systems undergoing first-order phase transitions, such as the NI phase transition, is demanding due to the substantial energy gap between the two phases. To address this, gREM, specifically designed for first-order phase transitions, was utilized to enhance sampling near the NI phase transition temperature. Free-energy calculations based on the energy representation (ER) theory were employed to characterize the NI phase transition. ER evaluates the insertion free energy of $n$CB molecule for both nematic and isotropic phases, revealing a change in the temperature dependence across the NI phase transition. Further decomposition into intermolecular interaction energetic and entropic terms shows quantitatively the balance between these contributions at the NI phase transition temperature.

cond-mat.soft↗

arXiv2024

Persistent Homology Reveals the Role of Stiffness in Forming Topological Glasses in Dense Solutions of Ring Polymers

Ring polymers are characterized by topology-specific entanglements called threadings. In the limit of large rings, it is conjectured that a "topological glass" should emerge due to the proliferation of threadings. In this study, we used persistent homology to quantify threading structures of ring polymers with different chain stiffness and elucidate mechanisms behind topological glasses. Using coordination data from coarse-grained molecular dynamics simulations, we analyzed the topology of the union of virtual spheres centered on each monomer or center of mass. As the radius of each sphere increases, the corresponding points connect, giving rise to topological entities such as edges, loops, and facets. We then analyzed how the number of loops per ring chain and penetrated loops varies with sphere radius, focusing on the effects of chain stiffness and density. The results reveal that loops are larger in stiff ring chains, whereas flexible ring chains do not generate sufficiently large loops to establish a threading structure. The stiffness of ring polymer plays a significant role in the formation of topological glasses in ring polymers.

cond-mat.soft↗

arXiv2024

Influence of cholesterol on hydrogen-bond dynamics of water molecules in lipid-bilayer systems at varying temperatures

Cholesterol (Chol) plays a crucial role in shaping the intricate physicochemical attributes of biomembranes, exerting considerable influence on water molecules proximal to the membrane interface. In this study, we conducted molecular dynamics simulations on the bilayers of two lipid species, dipalmitoyl phosphatidylcholine (DPPC) and palmitoyl sphingomyelin (PSM); they are distinct with respect to the structures of the hydrogen-bond (H-bond) acceptors. Our investigation focuses on the dynamic properties and H-bonds of water molecules in the lipid-membrane systems, with particular emphasis on the influence of Chol at varying temperatures. Notably, in the gel phase at 303 K, the presence of Chol extends the lifetimes of H-bonds of the oxygen atoms acting as H-bond acceptors within DPPC with water molecules by a factor of 1.5 to 2.5. In the liquid-crystalline phase at 323 K, on the other hand, H-bonding dynamics with lipid membranes remain largely unaffected by Chol. This observed shift in H-bonding states serves as a crucial key to unraveling the subtle control mechanisms governing water dynamics in lipid-membrane systems.

cond-mat.soft↗

arXiv2024

A methodology of quantifying membrane permeability based on returning probability theory and molecular dynamics simulation

We propose a theoretical approach to estimate the permeability coefficient of substrates (permeants) for crossing membranes from donor (D) phase to acceptor (A) phase by means of molecular dynamics (MD) simulation. A fundamental aspect of our approach involves reformulating the returning probability (RP) theory, a rigorous bimolecular reaction theory, to describe permeation phenomena. This reformulation relies on the parallelism between permeation and bimolecular reaction processes. In the present method, the permeability coefficient is represented in terms of the thermodynamic and kinetic quantities for the reactive (R) phase that exists within the inner region of membranes. One can evaluate these quantities using multiple MD trajectories starting from phase R. We apply the RP theory to the permeation of ethanol and methylamine at different concentrations (infinitely dilute and 1 mol% conditions of permeants). Under the 1 mol% condition, the present method yields a larger permeability coefficient for ethanol ($0.12 \pm 0.01 ~\mathrm{cm~s^{-1}}$) than for methylamine ($0.069\pm 0.006~\mathrm{cm~s^{-1}}$), while the values of the permeability coefficient are satisfactorily close to those obtained from the brute-force MD simulations [$0.18\pm 0.03 ~\mathrm{cm~s^{-1}}$ and $0.052 \pm 0.005 ~\mathrm{cm~s^{-1}}$ for ethanol and methylamine, respectively]. Moreover, upon analyzing the thermodynamic and kinetic contributions to the permeability, we clarify that a higher concentration dependency of permeability for ethanol, as compared to methylamine, arises from the sensitive nature of ethanol's free-energy barrier within the inner region of the membrane against ethanol concentration.

cond-mat.soft↗

arXiv2024

Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach

The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C$7_{\mathrm{eq}}$ ($β$-sheet structure) and C$7_{\mathrm{ax}}$ (left handed $α$-helix structure), have been primarily characterized using the main chain dihedral angles, $φ$ (C-N-C$_α$-C) and $ψ$ (N-C$_α$-C-N). However, our recent deep learning combined with "Explainable AI" (XAI) framework has shown that the transition state can be adequately captured by a free energy landscape using $φ$ and $θ$ (O-C-N-C$_α$) [T. Kikutsuji, et al. J. Chem. Phys. 156, 154108 (2022)]. In perspective of extending these insights to other collective variables, a more detailed characterization of transition state is required. In this work, we employ the interatomic distances and bond angles as input variables for deep learning, rather than the conventional and more elaborate dihedral angles. Our approach utilizes deep learning to investigate whether changes in the main chain dihedral angle can be expressed in terms of interatomic distances and bond angles. Furthermore, by incorporating XAI into our predictive analysis, we quantified the importance of each input variable and succeeded in clarifying the specific interatomic distance that affects the transition state. The results indicate that constructing a free energy landscape based on using the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.

physics.chem-ph↗

arXiv2023

Unraveling the glass-like dynamic heterogeneity in ring polymer melts: From semi-flexible to stiff chain

Ring polymers are an intriguing class of polymers with unique physical properties, and understanding their behavior is important for developing accurate theoretical models. In this study, we investigate the effect of chain stiffness and monomer density on static and dynamic behaviors of ring polymer melts using molecular dynamics simulations. Our first focus is on the non-Gaussian parameter of center of mass displacement as a measure of dynamic heterogeneity, which is commonly observed in glass-forming liquids. We find that the non-Gaussianity in the displacement distribution increases with the monomer density and stiffness of the polymer chains, suggesting that excluded volume interactions between centers of mass have a stronger effect on the dynamics of ring polymers. We then analyze the relationship between the radius of gyration and monomer density for semi-flexible and stiff ring polymers. Our results indicate that the relationship between the two varies with chain stiffness, which can be attributed to the competition between repulsive forces inside the ring and from adjacent rings. Finally, we study the dynamics of bond-breakage virtually connected between the centers of mass of rings to analyze the exchanges of inter-molecular networks of bonds. Our results demonstrate that the dynamic heterogeneity of bond-breakage is coupled with the non-Gaussianity in ring polymer melts, highlighting the importance of bond-breaking method in determining the inter-molecular dynamics of ring polymer melts. Overall, our study provides insights into the fundamental mechanism of ring polymers and sheds light on the factors that govern their dynamic behavior.

cond-mat.soft↗

arXiv2023

Revealing the hidden dynamics of confined water in acrylate polymers: Insights from hydrogen-bond lifetime analysis

Polymers contain functional groups that participate in hydrogen bond (H-bond) with water molecules, establishing a robust H-bond network that influences bulk properties. This study utilized molecular dynamics (MD) simulations to examine the H-bonding dynamics of water molecules confined within three poly(meth)acrylates: poly(2-methoxyethyl acrylate) (PMEA), poly(2-hydroxyethyl methacrylate) (PHEMA), and poly(1-methoxymethyl acrylate) (PMC1A). Results showed that H-bonding dynamics significantly slowed as the water content decreased. Additionally, the diffusion of water molecules and its correlation with H-bond breakage were analyzed. Our findings suggest that when the H-bonds between water molecules and the methoxy oxygen of PMEA are disrupted, those water molecules persist in close proximity and do not diffuse on a picosecond timescale. In contrast, the water molecules H-bonded with the hydroxy oxygen of PHEMA and the methoxy oxygen of PMC1A diffuse concomitantly with the breakage of H-bonds. These results provide an in-depth understanding of the impact of polymer functional groups on H-bonding dynamics.

cond-mat.soft↗