Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA neural network interatomic potential for the phase change material GeTe
127
0
0.0
(
0
)
Added by
Gabriele Cesare Sosso M.sc.
Publication date
2012
fields
Physics
and research's language is
English
Authors
Gabriele C. Sosso
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
An interatomic potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for Al-Tb alloy. We show the obtained DNN model can well reproduce the energies and forces calculated by AIMD. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of Al90Tb10 liquid, such as the partial pair correlation functions (PPCFs) and the bond angle distributions, in comparison with the results from AIMD. Furthermore, the developed DNN interatomic potential predicts the formation energies of crystalline phases of Al-Tb system with the accuracy comparable to ab initio calculations. The structure factor of Al90Tb10 metallic glass obtained by MD simulation using the developed DNN interatomic potential is also in good agreement with the experimental X-ray diffraction data.
The prototypical phase change material GeTe shows an enigmatic phase transition at Tc ca. 650 K from rhombohedral (R3m) to cubic (Fm-3m) symmetry. While local probes see little change in bonding, in contrast, average structure probes imply a displacive transition. Here we use high energy X-ray scattering to develop a model consistent with both the local and average structure pictures. We detect a correlation length for domains of the R3m structure which shows power law decay upon heating. Unlike a classical soft mode, it saturates at ca. 20 {AA} above Tc. These nanoclusters are too small to be observed by standard diffraction techniques, yet contain the same local motif as the room temperature structure, explaining previous discrepancies. Finally, a careful analysis of the pair distribution functions implies that the 0.6 % negative thermal expansion (NTE) at the R3m -Fm-3m transition is associated with the loss of coherence between these domains.
Oxygen is widely used to tune the performance of chalcogenide phase-change materials in the usage of phase-Change random access memory (PCRAM) which is considered as the most promising next-generation non-volatile memory. However, the microscopic role of oxygen in the write-erase process, i.e., the reversible phase transition between crystalline and amorphous state of phase-change materials is not clear yet. Using oxygen doped GeTe as an example, this work unravels the role of oxygen at the atomic scale by means of ab initio total energy calculations and ab initio molecular dynamics simulations. Our main finding is that after the amorphization and the subsequent re-crystallization process simulated by ab initio molecular dynamics, oxygen will drag one Ge atom out of its lattice site and both atoms stay in the interstitial region near the Te vacancy that was originally occupied by the oxygen, forming a dumbbell-like defect (O-VTe-Ge), which is in sharp contrast to the results of ab initio total energy calculations at 0 K showing that the oxygen prefers to substitute Te in crystalline GeTe. This specific defect configuration is found to be responsible for the slower crystallization speed and hence the improved data retention of oxygen doped GeTe as reported in recent experimental work. Moreover, we find that the oxygen will increase the effective mass of the carrier and thus increases the resistivity of GeTe. Our results unravel the microscopic mechanism of the oxygen-doping optimization of phase-change material GeTe, and the present reported mechanism can be applied to other oxygen doped ternary chalcogenide phase-change materials.
Neural network potentials (NNPs) combine the computational efficiency of classical interatomic potentials with the high accuracy and flexibility of the ab initio methods used to create the training set, but can also result in unphysical predictions when employed outside their training set distribution. Estimating the epistemic uncertainty of an NNP is required in active learning or on-the-fly generation of potentials. Inspired from their use in other machine-learning applications, NNP ensembles have been used for uncertainty prediction in several studies, with the caveat that ensembles do not provide a rigorous Bayesian estimate of the uncertainty. To test whether NNP ensembles provide accurate uncertainty estimates, we train such ensembles in four different case studies, and compare the predicted uncertainty with the errors on out-of-distribution validation sets. Our results indicate that NNP ensembles are often overconfident, underestimating the uncertainty of the model, and require to be calibrated for each system and architecture. We also provide evidence that Bayesian NNPs, obtained by sampling the posterior distribution of the model parameters using Monte-Carlo techniques, can provide better uncertainty estimates.
Potentials that could accurately describe the irradiation damage processes are highly desired to figure out the atomic-level response of various newly-discovered materials under irradiation environments. In this work, we introduce a deep-learning interatomic potential for monolayer MoS2 by combining all-electron calculations, an active-learning sampling method and a hybrid deep-learning model. This potential could not only give an overall good performance on the predictions of near-equilibrium material properties including lattice constants, elastic coefficients, energy stress curves, phonon spectra, defect formation energy and displacement threshold, but also reproduce the ab initial irradiation damage processes with high quality. Further irradiation simulations indicate that one single highenergy ion could generate a large nanopore with a diameter of more than 2 nm, or a series of multiple nanopores, which is qualitatively verified by the subsequent 500 keV Au+ ion irradiation experiments. This work provides a promising and feasible approach to simulate irradiation effects in enormous newly-discovered materials with unprecedented accuracy.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
