論文

A concept of nanoscale bearing structures utilizing nanocarbon materials is presented based on the prediction via molecular dynamics simulations. The proposed mechanism consists of a graphene layer, intercalated single walled carbon nanotubes (SWNTs), and substrate graphenes. It is found that the friction against the movement of the uppermost graphene is smallest for the 1 SWNT model. © 2010 Materials Research Society.

© 2014 Taylor and Francis. Core-shell nanoparticles are nanosized particles that consist of a core and a shell, constructed from different metallic elements. Core-shell nanoparticles have received extensive attention, owing to their various potential applications such as paints, optical films and catalysts. Herein, we investigate the melting behaviours of different core-shell nanoparticles under continuous heating using molecular dynamics simulation. Different metallic elements were examined as core-shell and pure nanoparticles. Five different processes were observed during the melting of core-shell nanoparticles. In contrast, only one process was identified during the melting of pure nanoparticles. These processes were influenced by the nanoparticle size, shell thickness and differences between the lattice constants and melting point temperatures of the metallic elements. Our simulation provides microscopic insights into the melting behaviours of existing and proposed core-shell nanoparticles that would be highly beneficial towards the fabrication of materials with different chemical coatings.

© 2019 The Royal Society of Chemistry. We have performed coarse-grained molecular simulations to investigate the morphologies and phase diagrams of self-assembled spheroidal triblock Janus nanoparticles (JNPs) confined in nanotubes. Nanoparticles exhibit several unique behaviours through shape changes, such as high thermal conductivity in heat pipes, high strength of binding avidity in ligand-receptor interactions, high amounts of uptake between cells during endocytosis processes, and so on. In this study, we focus on the effect of shape on self-assembled structures or phases within nanospaces. We derived qualitative phase diagrams of axial pressure versus the ratio of the diameter of the nanoparticles and nanotubes. Two distinct types of nanotube walls and three types of JNP shapes were considered: hydrophobic and hydrophilic walls, and NP aspect ratios of 1.5, 2.0, and 2.5. We observed diverse morphologies in spheroidal JNP solutions, many of which have not been observed before in bulk solutions. We also compared the self-assembled structures of spherical triblock JNPs. By increasing the aspect ratio of the NPs, clusters of unique structures, such as tripods, tetrapods, and hexapods, were formed. Moreover, we found that an increase in the value of the aspect ratio leads to a shift in the multilayer toroidal stacking phases to a weaker confinement effect. The self-assembly behaviours of the spheroidal JNPs can be qualitatively predicted based on the chemical nature of the nanotube walls and shapes of the JNPs.

© 2018 Elsevier B.V. The prediction and control of penetration in a polymeric membrane is of critical importance in green chemistry and energy technology, including gas separation, water purification, and desalination. We performed molecular simulations of water transport through a polymeric membrane to clarify the key factors that dominate water permeation. The effects of additives and chemical interaction (solubility) on water inhibition were investigated. We found that additives reduce water permeability into the membrane. Upon incorporation of the additive, strength of coordination of water molecules near the membrane surface increases. Thus, the penetration frequency of water molecules into the membrane decreases. It is suggested that the local environment near the membrane surface plays a significant role in controlling water permeability. In order to gain deeper insights into the polymer design, we discussed the chemical interaction (solubility) parameter change between polymer chains and additives. Using a repulsive chemical species of a polymer chain for additives can lead to higher water inhibition. The ability to control water permeability into the membrane by polymer design can be exploited for applications in water separation technology.

The success of machine learning combined with molecular simulation has been demonstrated in predicting electronic structure of materials(1)(2). A breakthrough that can aid in the design of new molecules that can be used to enhance the performance of material science. However, the prediction of physical properties of functional materials is one of the most challenging issue because of a multiscale problem across scientific disciplines in soft matter systems(3). In this study, first we used the coarse-grained molecular simulation to solve the multiscale problem. Second, we determined whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results will aid in further our scientific understanding and gain new insights(4).

分子シミュレーション研究会発足20周年を記念して，「若手研究者より研究会に期待すること」をまとめたので報告する．分子シミュレーション夏の学校，分子シミュレーションスクールでアンケートを実施し，その内容をまとめて本記事を執筆した．それぞれ28人，52人から有効なご回答をいただいた．ここでは興味深い意見を中心に取り上げさせていただく．

We have performed coarse-grained molecular simulation simulations to investigate self-assembly processes and structures of functionalized nanoparticles confined in a nanoslit. The functionalized nanoparticle is decorated with some attractive areas. On the basis of the observed temperature- and concentration-dependent morphologies, we derive semiquantitative phase diagrams of the temperature. Five distinct designs of the nanoparticles are considered. We find that various self-assembled structures depending on the number of functionalized groups. The self-assembly behavior of the functionalized nanoparticles can be qualitatively predicted when the number of groups is known. Our simulation offers a theoretical guide to control morphologies of the self-assembled nanoparticles, a novel system that may find applications in nanofluidic devices or for nanopatterning.

近年，コンピュータシミュレーション技術の発達によって，これまで取り扱うことのできなかった様々なシステムの再現，可視化が行われるようになってきた．散逸粒子動力学(DPD)法は分子シミュレーションの一種だが，原子や分子をある程度一纏めとして扱うことで，従来の分子シミュレーションよりも大きな時空間スケールの現象を再現できる．本稿では，DPD法の歴史，アルゴリズムを簡単に説明したあと，これまで筆者が対象としてきたいくつかのソフトマター材料(界面活性剤，ナノ粒子，液晶，高分子膜・生体膜)に対するDPDシミュレーションについて紹介する．

近年，合成技術の発達によって様々な形状や表面を有すナノ粒子の作製が可能となった．そのようなナノ粒子は複雑な自己集合構造を示すことが報告されている．ナノ粒子の自己集合の制御や予測は，分子レベルからの機能性材料の設計に必要不可欠である．本研究では，両親媒性テトラポッド型ナノ粒子に着目し，腕の長さや親水/疎水比が自己集合へどのような影響を及ぼすか，散逸粒子動力学シミュレーションによって調べた．

Blue phases (BPs) are three-dimensional self-assembly structures of liquid crystals with a lattice of line defects. They have attracted considerable interest as the potential in next generation displays. It is well known that BPs occur in cholesteric liquid crystals (CLCs) under certain thermodynamic conditions. However, previous studies have indicated that confining surfaces may induce distinctive structural changes. For example, a quasi-two-dimensional (Q2D) confinement system was investigated with the aid of numerical calculations, and a stable Q2D Skyrmion structure was attained. In this study, we investigated the CLC phase behavior at the molecular scale for a quasi-one-dimensional (Q1D) nanotube system using molecular simulations. We observed the various morphological behaviors of CLCs by changing the temperature and the radius of nanotube. Specially, we discovered a self-assembly structures with cylindrical defects rather than lines by introducing a novel local orientation analysis. Our results show that the self-assembly of CLCs offers a guide to control the intensity in one-dimensional confinement and fundamental knowledge for application to optical devices.

We investigate the structure and rheological properties of dilute colloid-polymer mixtures at rest and under shear via molecular simulations that take into account hydrodynamic interactions. Mixtures of amphiphilic Janus colloids (JCs) and hydrophobic/amphiphilic polymers are considered for various solvent qualities and polymer concentrations. Free polymers, small colloidal micelles, and small hybrid aggregates coexist in mixtures with slightly hydrophobic homopolymers. As the solvent quality worsens, all polymers aggregate into small droplets, covered and stabilized by the JCs. In mixtures with amphiphilic polymers, we observe the coexistence of free polymers, purely polymeric micelles, and hybrid aggregates. At small shear rates, all mixtures exhibit a Newtonian-like response with intrinsic shear viscosities that are up to two times as large as of pure suspensions of non-adsorbing colloids at the same concentration. Further, the mean aggregation number increases slightly due to the flow-enhanced collision of aggregates. At larger shear rates, however, the aggregates break up, the polymers align in the flow direction, and the mixtures become shear-thinning. This shear-induced breakup occurs at stronger shear compared to pure JC suspensions, indicating that the adsorbed polymers reinforce the hybrid aggregates.

ソフトマターは与えられた外力に対して特異な応答を示す。この性質を利用し，洗剤や化粧品，ゴムタイヤ，液晶ディスプレイなどさまざまな機能性材料として用いられている。これらの材料の性質は分子構造だけでなく，それらが集まって形成される自己集合構造にも強く影響することが知られている。ゆえに所望の機能をもつ材料の設計には，ソフトマターの自己集合構造を予測・制御することが重要となる。われわれは粗視化分子シミュレーションを用い，さまざまなソフトマターの自己集合挙動，さらには自己集合構造と物性の関係性について研究してきた。ここではこれまで取り組んできた研究の中からいくつかについて紹介する。

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.