Understanding of the nanoparticle (NP) sintering mechanism at the atomic scale is of significance for improving various NP applications, such as printable nanoinks, catalysts, and electrode materials in energy devices. In this research, sintering dynamics of Cu–Ag core–shell NPs with various geometries are investigated through molecular dynamics simulations under different temperatures. The evolutions of local crystalline structure, characterized by common neighbor analysis, and potential energy during the sintering are studied to identify the sintering mechanisms. Sintering of two equally sized NPs is divided into three stages according to the shrinkage evolution, and depending on the sintering stage and condition, NP undergoes reorientation for achieving epitaxial layering, plastic deformation, surface diffusion, wetting, and crystallization–amorphization–recrystallization. Although the Cu core is coalescent neither in solid phase nor in surface-premelting-induced sintering, it can enhance the mobility of Ag shell atoms. The size-dependent optimal core radius/shell thickness ratio is proposed to achieve maximum densification and thus maximum bonding strength at room temperature.

Ab initio molecular dynamics (AIMD) is a versatile and reliable computational approach to atomic-scale materials science. However, due to the expensive computational cost on the first-principles calculation at each time step, the temporal and spatial scales are significantly limited, hindering its broader applications. Therefore, to accelerate the simulation clock of AIMD, atomic data production in AIMD using a recurrent neural network (RNN) is studied in this research. We demonstrate the feasibility of incorporating RNN-predicted time steps in AIMD, while maintaining its accuracy. The RNN models, which are trained using AIMD simulation results, directly predict atomic velocities and positions of Si atoms, reducing errors by decoupling the position and velocity update procedures from the Newtonian mechanics. Not only the predicted atomic data but also material properties calculated using the predicted data, such as the radial distribution function, temperature, velocity autocorrelation function, phonon density of states, and heat capacity, exhibit excellent agreements with the ground-truth AIMD calculations. Since the RNN prediction is much faster than the first-principles calculation of AIMD, this approach is expected to effectively accelerate AIMD, contributing to computational materials research.

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