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Melting temperature is a fundamental material property and is defined as the temperature at which the solid and liquid phases have the same free energy. However, there is no systematic study employing atomic simulations to calculate melting temperature using this definition. Here, molecular dynamics simulations and nonequilibrium thermodynamic integration methods are combined to calculate the melting temperature of Al and Cu. Results show that to accurately obtain the melting temperature, the model size should be considered carefully because the free energies of both solid and liquid phases are inversely proportional to the model size, causing a model size dependence on the calculated melting temperature. In addition, the melting temperature for various (semi‐) empirical potential models for Al and Cu is calculated and verified against experimental values to provide guidelines for the choice of potential models for simulation‐based problems relevant to the solid–liquid phase transformation. [ABSTRACT FROM AUTHOR]

Free energies of a solid surface and a solid–liquid interface play significant roles in thermodynamics. Due to the limited availability of experimental data, computational methods offer effective alternatives for calculating these properties. This study adopts advanced frameworks of the logarithmic mean force dynamics method to present an enhanced sampling approach for the calculation of the free energy at different temperatures. To achieve this, the free energy profile is constructed along with a pre‐established collective variable within the melting transition and cleavage processes. The values of the solid surface and solid–liquid interface free energies are then extrapolated from the excess free energy related to the formation and persistence of the solid surface or the solid–liquid interface. Furthermore, this methodology is employed to calculate the temperature dependence of the free energy measurements for the (100) and (110) surfaces and interfaces of Cu. It is shown that this methodology is robust and readily applicable in contemporary models of atomic interactions and various systems. [ABSTRACT FROM AUTHOR]

The Poisson's ratio of two-dimensional materials such as graphene can be tailored by surface hydrogenation. The density and distribution of hydrogenation may significantly affect the Poisson's ratio of the graphene structure. Therefore, optimization of the distribution of hydrogenation is useful to achieve the structure with a targeted Poisson's ratio. For this purpose, we developed an inverse design algorithm based on machine learning using the XGBoost method to reveal the relationship between the Poisson's ratio and distribution of hydrogenation. Based on this relationship, we can optimize the hydrogenated graphene structure to have a low Poisson's ratio. Instead of performing molecular dynamic simulations for all possible structures, we could find the optimal structures using the search algorithm and save significant computational resources. This algorithm could successfully discover structures with low Poisson's ratios around −0.5 after only 1600 simulations in a large design space of approximately 5.2 × 106 possible configurations. Moreover, the optimal structures were found to exhibit excellent flexibility under compression of around −65% without failure and can be used in many applications such as flexible strain sensors. Our results demonstrate the applicability of machine learning to the efficient development of new metamaterials with desired properties. [ABSTRACT FROM AUTHOR]

With its excellent mechanical and thermal properties, bilayer graphane is a promising material for realizing future nanoelectromechanical systems. In this study, we focus on the auxetic behavior of bilayer graphane under external loading along various directions through atomistic simulations. We numerically and theoretically reveal the mechanism of the auxeticity in terms of intrinsic interactions between carbon atoms by constructing bilayer graphane. Given that the origin of the auxeticity is intrinsic rather than extrinsic, the work provides a novel technique to control the dimensions of nanoscale bilayer graphane by simply changing the external conditions without the requirement of complex structural design of the material. [ABSTRACT FROM AUTHOR]

Melting is a fundamental process, but the atomic-level comprehension of surface melting remains unclear. In this study, we introduce a parameter called the per-atom crystallinity parameter and utilize molecular dynamics simulations to investigate the melting behavior of aluminum surfaces. Our computational findings reveal that the melting initiates at the free surface and then spreads in a manner depending on the surface orientation. We identify two distinct mechanisms that govern the spreading of melting: the self-growth of liquid nuclei and the coalescence of neighboring liquid nuclei. Our simulations demonstrate that the coalescence mechanism predominates on the Al(110) surface, resulting in a flat melting front. In contrast, the melting front on the Al(111) surface exhibits a spherical-like shape due to the weaker influence of the coalescence mechanism. Overall, this study provides a more comprehensive understanding of the initiation of melting on crystalline surfaces. [Display omitted] • Melting starts at the free surface and its spreading depends on surface orientation. • Two mechanisms govern melting spread: self-growth and coalescence of liquid clusters. • The coalescence mechanism strongly affects the melting front shape. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Uniform pattern transformation can be observed in some structures with periodic arrays of pores at a critical compressive load because of buckling of the constituents of the structures. This pattern transformation can be exploited to design structures for various potential applications. Previous studies have focused on the instability of periodic porous structures of which the base materials were elastomers, and applications of these structures may be narrow because of the elastomer limitations of low melting temperature and stiffness. In addition, material failures such as plasticity and fracture were rarely discussed in previous studies. Here, we introduce metals as the base materials for some periodic metallic porous nanostructures (PMPNs). Our molecular dynamics simulation results show that PMPNs can exhibit pattern transformation at a critical strain because of buckling. In addition, we develop a simple formulation by incorporating the effect of surface on the Euler–Bernoulli beam theory to predict the critical load for the buckling of nanostructures. The prediction of our model is in good agreement with the molecular dynamics simulation results. When the applied strain is sufficiently large, the nanoscale metals experience dislocation-medicated plasticity. We also show that the pore shape of the PMPNs strongly affects the characteristics of the periodic metallic structures including the effective Young's modulus, critical strain for micro-buckling, and critical strain for plasticity. [ABSTRACT FROM AUTHOR]