Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5

Konstantinou, Konstantinos; Mocanu, Felix C.; Lee, Taehoon; Elliott, S. R.

doi:10.1038/s41467-019-10980-w

Cited by 107 publications

(112 citation statements)

References 67 publications

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“…(b) shows a fivefold Ge site, also derived from a distorted octahedral Ge site. This fivefold Ge site resembles the fivefold Ge site found by Konstantiou et al25 for defects in a GeSbTe alloy. Thus, some configurations in the excited state of GeSe resemble those in the ground state of GeTe.…”

supporting

confidence: 81%

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Structural changes during the switching transition of chalcogenide selector devices

Guo

Zhang

et al. 2019

Applied Physics Letters

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Ovonic threshold switches are a favored choice for chalcogenide-based amorphous (a-) GeSex selector devices used in cross-point arrays of nonvolatile memories. Previous models of their nonlinear high-field conduction proposed a largely electronic-only switching mechanism, within a fixed density of electronic states. Here, we use a density functional molecular-dynamics supercell calculation to show that the high-current excited state configuration of a-GeSex has structural changes such as additional Ge-Ge bonds and overcoordinated Ge sites, giving lower effective mass, more delocalized conduction states, and a lower ON resistance.

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supporting

confidence: 81%

“…Defects observed by photomodulation spectroscopy of the amorphous phase pin the Fermi level near midgap, 27,28 giving a high resistance OFF state. The nature of these defects has been only recently becoming clearer, 25 and it must also be related to those of OTS materials.…”

Section: 45mentioning

confidence: 99%

Structural changes during the switching transition of chalcogenide selector devices

Guo

Zhang

et al. 2019

Applied Physics Letters

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“…This is done by means of mathematical descriptors which encode the information of the local atomic environments, to be retrieved and interpolated for newly encountered configurations during MD implementations [7,8]. This brings MD simulations of systems containing thousands of atoms with DFT accuracy into reality [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Recently, an ML interatomic potential was developed for Si based on the Gaussian approximation potential (GAP) framework [28].…”

mentioning

confidence: 99%

Insights into the primary radiation damage of silicon by a machine learning interatomic potential

Hamedani

Byggmästar

Djurabekova

et al. 2020

Materials Research Letters

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We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. IMPACT STATEMENT Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.

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“…[28] Open questions in applying future generations of ML potentials to PCMs fall into two categories, defined by the chemical composition. [133] Besides the bulk phases ( Figure 3c) and nanowires (Figure 3d) discussed in this Progress Report, it would be highly valuable to understand more thoroughly the atomistic structure and behavior of superlattice structures, [134] continuing along the lines of existing DFT studies [135] but now on larger length scales. The nature of defect states in amorphous Ge 2 Sb 2 Te 5 has been studied very recently using a large number of structural models generated in ML-driven simulations.…”

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5

Cited by 107 publications

References 67 publications

Structural changes during the switching transition of chalcogenide selector devices

Structural changes during the switching transition of chalcogenide selector devices

Insights into the primary radiation damage of silicon by a machine learning interatomic potential

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

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