Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSparse selection of bases in neural-network potential for crystalline and liquid Si
91
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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 surface layer undergoes a first-order solid-solid phase transition occurring at $371 ^{circ}$C. The crystalline phase observed for T$>$371 $^{circ}$C is stable up to at least 430 $^{circ}$C. Grazing Incidence X-ray Diffraction data at T$>$371 $^{circ}$C imply lateral order comprising two coexisting phases of different oblique unit cells, in stark contrast with the single phase with a rectangular unit cell found for low-temperature crystalline phase $359 ^{circ}$C$<T<371 ^{circ}$C.
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 potential for aluminum that overcomes the limitations inherent to DFT, and we use it to obtain accurate anharmonic vacancy formation free energies as a function of temperature. While confirming the important role of anharmonicity at high temperatures, the calculation unveils a markedly nonlinear behavior of the vacancy formation entropy and shows that the vacancy formation free energy only violates Arrhenius law at temperatures above 600 K, in contrast with previous DFT calculations.
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) representation of the potential-energy surface obtained from reference calculations based on density functional theory. It is demonstrated that the NN potential provides a close to ab initio quality description of a number of properties of liquid, crystalline and amorphous GeTe. The availability of a reliable classical potential allows addressing a number of issues of interest for the technological applications of phase change materials, which are presently beyond the capability of first principles molecular dynamics simulations.
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 estimated value for intrinsic (1E14 cm-3) c-Si. The deviation observed with respect to elastic modulus remarkably matches with the estimated value while considering the electronic elastic strain effect on carrier concentration as an influence of negative pressure coefficient of band gap for Si. The value is predominantly higher than the reported value of a decrease of 1-3 percent in stiffness as an effect of impurity in c-Si.
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 ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way, that a DNN model, trained on meta-GGA (SCAN) data, reduce the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
