Defect-free and crystallinity-preserving ductile deformation in semiconducting AgS.

Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (AgS), which is defect-free, omni-directional, and preserving perfect crystallinity.

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides.

We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-AgSe, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of AgSe to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated.

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials.

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe shock phenomena.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-AgSe.

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)].

Dynamic asymmetry of self-diffusion in liquid ZnCl2 under pressure: an ab initio molecular-dynamics study.

The static and dynamic properties of liquid ZnCl2 under pressure are investigated by ab initio molecular-dynamics simulations. The pressure range covers ambient to approximately 80 GPa. The ZnCl4 tetrahedra, which are rather stable at ambient pressure, are shown to deform and collapse with increasing pressure while maintaining an almost constant nearest-neighbor distance between Zn and Cl atoms.