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The neural-network interatomic potential for crystalline and liquid Si has been developed using the forward stepwise regression technique to reduce the number of bases with keeping the accuracy of the potential. This approach of making the neural-network potential enables us to construct the accurate interatomic potentials with less and important bases selected systematically and less heuristically. The evaluation of bulk crystalline properties, and dynamic properties of liquid Si show good agreements between the neural-network potential and ab-initio results.
A two dimensional crystalline layer is found at the surface of the liquid eutectic Au$_{82}$Si$_{18}$ alloy above its melting point $T_M=359 ^{circ}$C. Underlying this crystalline layer we find a layered structure, 6-7 atomic layers thick. This surfa
Lattice anharmonicity is thought to strongly affect vacancy concentrations in metals at high temperatures. It is however non-trivial to account for this effect directly using density functional theory (DFT). Here we develop a deep neural network pote
GeTe is a prototypical phase change material of high interest for applications in optical and electronic non-volatile memories. We present an interatomic potential for the bulk phases of GeTe, which is created using a neural network (NN) representati
We report the observation of carrier mediated decrease in the stiffness of crystalline (c)-Si(100) under nanoindentation. The apparent elastic modulii of heavily dopes (1E21 cm-3) p- and n-type c-Si are observed to be lower by 5.-7.5 percent that the
We compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic gro