Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

Gabardi, Silvia; Baldi, Edoardo; Bosoni, Emanuele; Campi, Davide; Caravati, S.; Sosso, Gabriele C.; Behler, Jörg; Bernasconi, Marco

doi:10.1021/acs.jpcc.7b09862

Cited by 49 publications

(68 citation statements)

References 94 publications

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“…[111] Reproduced with permission. [124] Reproduced with permission. c) An illustration of the relevant system sizes, here for the ternary PCM Ge 2 Sb 2 Te 5 , emphasizing the capabilities of ML potentials.…”

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

“…[111] Copyright 2014, Springer Nature. [124] Copyright 2017, American Chemical Society. [129] The small cell is one that is accessible to DFT-MD; the larger is one that is accessible to ML potentials; both are drawn to scale.…”

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

“…DFT-MD simulations are therefore one of the primary approaches for obtaining insight into amorphous PCMs. [122][123][124] This large structural diversity makes it very difficult to create empirical potential models for PCMs. [122][123][124] This large structural diversity makes it very difficult to create empirical potential models for PCMs.…”

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

“…[128] In contrast, an ML potential readily makes system sizes of many thousand atoms accessible, as we show for a structural model of bulk amorphous (a-) Ge 2 Sb 2 Te 5 , containing 7200 atoms (Figure 3c), [129] and of a partially melted GeTe nanowire, described by an even more complex structural model with over 16 000 atoms (Figure 3d). [124] An NN potential for GeTe was introduced by Sosso et al in 2012 already. [42] The first and most central application was in modeling crystallization (the atomistic process leading from a "zero" to a "one" in digital memories).…”

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

“…d) Simulation of a GeTe nanowire, reaching device-size models: in this case, the center of the hexagonal wire has been liquefied by heating whereas the bottom part was kept fixed in the crystalline structure during the simulation. [124] Reproduced with permission. [124] Copyright 2017, American Chemical Society.…”

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

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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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Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

Section: Structure Dynamics and Function In Phase-change Materialsmentioning

confidence: 99%

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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Atomistic Study of the Configurational Entropy and the Fragility of Supercooled Liquid GeTe

Chen,

Campi,

Bernasconi

et al. 2024

Adv Funct Materials

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Phase‐change materials (PCMs) are an important family of alloys employed in non‐volatile memories and neuromorphic devices. These devices exploit the ability of PCMs to undergo rapid transitions between amorphous and crystalline phases at moderately high temperature. At ambient conditions, both phases are stable. These properties imply a very strong temperature dependence of the crystallization kinetics, which has been attributed to the high fragility of the supercooled liquid phase. In this work, the fragility index of GeTe, an important PCM, is extracted from a computational exploration of the potential energy landscape of the system by molecular dynamics simulations. For this purpose, a neural‐network potential is used to describe the interatomic interactions, which enables the evaluation of the configurational entropy in the deeply supercooled regime. The viscosity and the relaxation times are also calculated in the same temperature range. Finally, the Adam‐Gibbs relation is used to extrapolate the data to low temperatures and estimate the glass transition temperature and the fragility index. The latter quantity turns out to be of the order of 135–140, which shows that liquid GeTe is a highly fragile system.

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Local Structural Origin of the Crystallization Tendency of Pure and Alloyed Sb

Ronneberger

Chen

Zhang

et al. 2018

Physica Rapid Research Ltrs

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Phase change materials are highly important for technological applications in data storage. This work is focused on the family of phase-change materials comprising antimony alloys. The crystallization of amorphous models of Sb, Ge 15 Sb 85 , and In 15 Sb 85 generated by simulated quenching from the melt is investigated. The structural properties of these alloys are also analyzed and their crystallization kinetics is elucidated in terms of the local structural motifs.

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Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

Cited by 49 publications

References 94 publications

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Atomistic Study of the Configurational Entropy and the Fragility of Supercooled Liquid GeTe

Local Structural Origin of the Crystallization Tendency of Pure and Alloyed Sb

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