Shock-induced plasticity and phase transformation in single crystal magnesium: An interatomic potential and non-equilibrium molecular dynamics simulations

An effective and reliable Finnis-Sinclair (FS) type potential is developed for large-scale molecular dynamics (MD) simulations of plasticity and phase transition of Magnesium (Mg) single crystals under high-pressure shock loading. The shock-wave profiles exhibit a split elastic-inelastic wave in the [0001]HCP shock orientation and a three-wave structure in the [10-10]HCP and [-12-10]HCP directions, namely, an elastic precursor following the plastic and phase-transition fronts. The shock Hugoniot of the particle velocity (Up) vs. the shock velocity (Us) of Mg single crystals in three shock directions under low shock strength reveals apparent anisotropy, which vanishes with increasing shock strength. For the [0001]HCP shock direction, the amorphization caused by strong atomic strain plays an important role in the phase transition and allows for the phase transition from an isotropic stressed state to the daughter phase. The reorientation in the shock directions [10-10]HCP and [-12-10]HCP, as the primary plasticity deformation, leads to the compressed hexagonal close-packed (HCP) phase and reduces the phase-transition threshold pressure. The phase-transition pathway in the shock direction [0001]HCP includes a preferential contraction strain along the [0001]HCP direction, a tension along [-12-10]HCP direction, an effective contraction and shear along the [10-10]HCP direction. For the [10-10]HCP and [-12-10]HCP shock directions, the phase-transition pathway consists of two steps: a reorientation and the subsequent transition from the reorientation hexagonal close-packed phase (RHCP) to the body-centered cubic (BCC). The orientation relationships between HCP and BCC are (0001)HCP á-12-10ñHCP // {110}BCC á001ñBCC. Due to different slipping directions during the phase transition, three variants of the product phase are observed in the shocked samples, accompanied by three kinds of typical coherent twin-grain boundaries between the variants. The results indicate that the highly concentrated shear stress leads to the crystal lattice instability in the elastic precursor, and the plasticity or the phase transition relaxed the shear stress.

Analysis of hypervelocity impacts: The tungsten case

The atomistic mechanisms of damage initiation during high velocity (v up to 9 km/s, kinetic energies up to 200 keV) impacts of W projectiles on a W surface have been investigated using parallel molecular-dynamics simulations involving large samples (up to 40 million atoms). Various aspects of the impact at high velocities, where the projectile and part of the target materials undergo massive plastic deformation, breakup, melting, and vaporization, are analyzed. Different stages of the penetration process have been identified through a detailed examination of implantation, crater size and volume, sputtered atoms, and dislocations created by the impacts. The crater volume increases linearly with the kinetic energy for a given impactor; and the total dislocation length increases with the kinetic energy but depends itself on the size of the impactor. Furthermore, the total dislocation length is less dependent of the fine details of the interatomic potential. The results are rationalized based on the physical properties of bcc W.

A generalizable machine learning potential of Ag-Au nanoalloys and its application t surface reconstruction, segregation and diffusion

Owing to the excellent catalytic properties of Ag-Au binary nanoalloys, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag-Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag-Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward the Ag-Au nanoalloy surface, where the empirical force field failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the empirical force fields. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database; therefore, the reported potential is expected to have a strong generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag-Au evolution, where accurate descriptions of free surfaces are necessary.

Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

Experimental observation of localized interfacial phonon modes

Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance. Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport. However, experimental validation is still lacking. Through a combination of Raman spectroscopy and high-energy-resolution electron energy-loss spectroscopy in a scanning transmission electron microscope, we report the experimental observation of localized interfacial phonon modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are further confirmed using molecular dynamics simulations with a high-fidelity neural network interatomic potential, which also yield thermal boundary conductance agreeing well with that measured in time-domain thermoreflectance experiments. Simulations find that the interfacial phonon modes have an obvious contribution to the total thermal boundary conductance. Our findings significantly contribute to the understanding of interfacial thermal transport physics and have impact on engineering thermal boundary conductance at interfaces in applications such as electronics thermal management and thermoelectric energy conversion.