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Acharya D, Abou El Kheir O, Campi D, Bernasconi M. Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe[Formula: see text]-like superlattices for phase change memories. Sci Rep 2024; 14:3224. [PMID: 38331918 PMCID: PMC10853215 DOI: 10.1038/s41598-024-53192-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/28/2024] [Indexed: 02/10/2024] Open
Abstract
Superlattices made of alternating blocks of the phase change compound Sb[Formula: see text]Te[Formula: see text] and of TiTe[Formula: see text] confining layers have been recently proposed for applications in neuromorphic devices. The Sb[Formula: see text]Te[Formula: see text]/TiTe[Formula: see text] heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb[Formula: see text]Te[Formula: see text] suffers from a low data retention due to a low crystallization temperature T[Formula: see text]. Substituting Sb[Formula: see text]Te[Formula: see text] with a phase change compound with a higher T[Formula: see text], such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe[Formula: see text] superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe[Formula: see text] heterostructures in neuromorphic devices with superior data retention.
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Affiliation(s)
- Debdipto Acharya
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy
| | - Omar Abou El Kheir
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy
| | - Davide Campi
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy
| | - Marco Bernasconi
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy.
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2
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Wintersteller S, Yarema O, Kumaar D, Schenk FM, Safonova OV, Abdala PM, Wood V, Yarema M. Unravelling the amorphous structure and crystallization mechanism of GeTe phase change memory materials. Nat Commun 2024; 15:1011. [PMID: 38307863 PMCID: PMC10837456 DOI: 10.1038/s41467-024-45327-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 01/17/2024] [Indexed: 02/04/2024] Open
Abstract
The reversible phase transitions in phase-change memory devices can switch on the order of nanoseconds, suggesting a close structural resemblance between the amorphous and crystalline phases. Despite this, the link between crystalline and amorphous tellurides is not fully understood nor quantified. Here we use in-situ high-temperature x-ray absorption spectroscopy (XAS) and theoretical calculations to quantify the amorphous structure of bulk and nanoscale GeTe. Based on XAS experiments, we develop a theoretical model of the amorphous GeTe structure, consisting of a disordered fcc-type Te sublattice and randomly arranged chains of Ge atoms in a tetrahedral coordination. Strikingly, our intuitive and scalable model provides an accurate description of the structural dynamics in phase-change memory materials, observed experimentally. Specifically, we present a detailed crystallization mechanism through the formation of an intermediate, partially stable 'ideal glass' state and demonstrate differences between bulk and nanoscale GeTe leading to size-dependent crystallization temperature.
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Affiliation(s)
- Simon Wintersteller
- Chemistry and Materials Design, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland
| | - Olesya Yarema
- Materials and Device Engineering, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland
| | - Dhananjeya Kumaar
- Chemistry and Materials Design, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland
| | - Florian M Schenk
- Chemistry and Materials Design, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland
| | | | - Paula M Abdala
- Laboratory of Energy Science and Engineering, Department of Mechanical and Process Engineering, ETH Zurich, 8092, Zürich, Switzerland
| | - Vanessa Wood
- Materials and Device Engineering, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland
| | - Maksym Yarema
- Chemistry and Materials Design, Institute for Electronics, Department of Information Technology and Electrical Engineering, ETH Zürich, 8092, Zürich, Switzerland.
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3
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Wu S, Yang X, Zhao X, Li Z, Lu M, Xie X, Yan J. Applications and Advances in Machine Learning Force Fields. J Chem Inf Model 2023; 63:6972-6985. [PMID: 37751546 DOI: 10.1021/acs.jcim.3c00889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Force fields (FFs) form the basis of molecular simulations and have significant implications in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is required to accurately describe system properties. However, an off-the-shelf FF may not be suitable for certain specialized systems, and researchers often need to tailor the FF that fits specific requirements. Before applying machine learning (ML) techniques to construct FFs, the mainstream FFs were primarily based on first-principles force fields (FPFF) and empirical FFs. However, the drawbacks of FPFF and empirical FFs are high cost and low accuracy, respectively, so there is a growing interest in using ML as an effective and precise tool for reconciling this trade-off in developing FFs. In this review, we introduce the fundamental principles of ML and FFs in the context of machine learning force fields (MLFF). We also discuss the advantages and applications of MLFF compared to traditional FFs, as well as the MLFF toolkits widely employed in numerous applications.
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Affiliation(s)
- Shiru Wu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xiaowei Yang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xun Zhao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Zhipu Li
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Min Lu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Xiaoji Xie
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
| | - Jiaxu Yan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), Nanjing 211816, P. R. China
- Changchun Institute of Optics, Fine Mechanics & Physics (CIOMP), Chinese Academy of Sciences, Changchun 130033, P. R. China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, P. R. China
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4
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Shen X, Zhou Y, Zhang H, Deringer VL, Mazzarello R, Zhang W. Surface effects on the crystallization kinetics of amorphous antimony. NANOSCALE 2023; 15:15259-15267. [PMID: 37674458 DOI: 10.1039/d3nr03536k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Elemental antimony (Sb) is regarded as a promising candidate to improve the programming consistency and cycling endurance of phase-change memory and neuro-inspired computing devices. Although bulk amorphous Sb crystallizes spontaneously, the stability of the amorphous form can be greatly increased by reducing the thickness of thin films down to several nanometers, either with or without capping layers. Computational and experimental studies have explained the depressed crystallization kinetics caused by capping and interfacial confinement; however, it is unclear why amorphous Sb thin films remain stable even in the absence of capping layers. In this work, we carry out thorough ab initio molecular dynamics (AIMD) simulations to investigate the effects of free surfaces on the crystallization kinetics of amorphous Sb. We reveal a stark contrast in the crystallization behavior between bulk and surface models at 450 K, which stems from deviations from the bulk structural features in the regions approaching the surfaces. The presence of free surfaces intrinsically tends to create a sub-nanometer region where crystallization is suppressed, which impedes the incubation process and thus constrains the nucleation in two dimensions, stabilizing the amorphous phase in thin-film Sb-based memory devices.
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Affiliation(s)
- Xueyang Shen
- Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Yuxing Zhou
- Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | - Hanyi Zhang
- Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | | | - Wei Zhang
- Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
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5
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Wang X, Sun S, Wang J, Li S, Zhou J, Aktas O, Xu M, Deringer VL, Mazzarello R, Ma E, Zhang W. Spin Glass Behavior in Amorphous Cr 2 Ge 2 Te 6 Phase-Change Alloy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302444. [PMID: 37279377 PMCID: PMC10427411 DOI: 10.1002/advs.202302444] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Indexed: 06/08/2023]
Abstract
The layered crystal structure of Cr2 Ge2 Te6 shows ferromagnetic ordering at the two-dimensional limit, which holds promise for spintronic applications. However, external voltage pulses can trigger amorphization of the material in nanoscale electronic devices, and it is unclear whether the loss of structural ordering leads to a change in magnetic properties. Here, it is demonstrated that Cr2 Ge2 Te6 preserves the spin-polarized nature in the amorphous phase, but undergoes a magnetic transition to a spin glass state below 20 K. Quantum-mechanical computations reveal the microscopic origin of this transition in spin configuration: it is due to strong distortions of the CrTeCr bonds, connecting chromium-centered octahedra, and to the overall increase in disorder upon amorphization. The tunable magnetic properties of Cr2 Ge2 Te6 can be exploited for multifunctional, magnetic phase-change devices that switch between crystalline and amorphous states.
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Affiliation(s)
- Xiaozhe Wang
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Suyang Sun
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Jiang‐Jing Wang
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Shuang Li
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Jian Zhou
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Oktay Aktas
- State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Ming Xu
- Wuhan National Laboratory for OptoelectronicsSchool of Integrated CircuitsHuazhong University of Science and TechnologyWuhan430074China
| | - Volker L. Deringer
- Department of ChemistryInorganic Chemistry LaboratoryUniversity of OxfordOxfordOX1 3QRUK
| | | | - En Ma
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Wei Zhang
- Center for Alloy Innovation and Design (CAID)State Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
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6
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Jakse N, Sandberg J, Granz LF, Saliou A, Jarry P, Devijver E, Voigtmann T, Horbach J, Meyer A. Machine learning interatomic potentials for aluminium: application to solidification phenomena. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 51:035402. [PMID: 36301702 DOI: 10.1088/1361-648x/ac9d7d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable byab initiomolecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states. Early stages of crystallization are further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to theab initioone. In both cases, a single step nucleation process is observed.
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Affiliation(s)
- Noel Jakse
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
| | - Johannes Sandberg
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Leon F Granz
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Anthony Saliou
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
| | - Philippe Jarry
- C-TEC, Parc Economique Centr'alp, 725 rue Aristide Bergès, CS10027, Voreppe 38341 CEDEX, France
| | - Emilie Devijver
- Université Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France
| | - Thomas Voigtmann
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Jürgen Horbach
- Institut für Theoretische Physik II, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Andreas Meyer
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Institut Laue-Langevin (ILL), 38042 Grenoble, France
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7
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Morrow JD, Deringer VL. Indirect learning and physically guided validation of interatomic potential models. J Chem Phys 2022; 157:104105. [DOI: 10.1063/5.0099929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Machine learning (ML) based interatomic potentials are emerging tools for material simulations, but require a trade-off between accuracy and speed. Here, we show how one can use one ML potential model to train another: we use an accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster potentials. Without the need for quantum-mechanical reference computations at the secondary stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the ML of interatomic potential models and suggests a route toward accelerated simulations of condensed-phase systems.
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Affiliation(s)
- Joe D. Morrow
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
| | - Volker L. Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
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8
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Xu Y, Zhou Y, Wang XD, Zhang W, Ma E, Deringer VL, Mazzarello R. Unraveling Crystallization Mechanisms and Electronic Structure of Phase-Change Materials by Large-Scale Ab Initio Simulations. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2109139. [PMID: 34994023 DOI: 10.1002/adma.202109139] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/17/2021] [Indexed: 06/14/2023]
Abstract
Ge-Sb-Te ("GST") alloys are leading phase-change materials for digital memories and neuro-inspired computing. Upon fast crystallization, these materials form rocksalt-like phases with large structural and vacancy disorder, leading to an insulating phase at low temperature. Here, a comprehensive description of crystallization, structural disorder, and electronic properties of GeSb2 Te4 based on realistic, quantum-mechanically based ("ab initio") computer simulations with system sizes of more than 1000 atoms is provided. It is shown how an analysis of the crystallization mechanism based on the smooth overlap of atomic positions kernel reveals the evolution of both geometrical and chemical order. The connection between structural and electronic properties of the disordered, as-crystallized models, which are relevant to the transport properties of GST, is then studied. Furthermore, it is shown how antisite defects and extended Sb-rich motifs can lead to Anderson localization in the conduction band. Beyond memory applications, these findings are therefore more generally relevant to disordered rocksalt-like chalcogenides that exhibit self-doping, since they can explain the origin of Anderson insulating behavior in both p- and n-doped chalcogenide materials.
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Affiliation(s)
- Yazhi Xu
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China
- Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, 52056, Germany
| | - Yuxing Zhou
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | - Xu-Dong Wang
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China
- Center for Alloy Innovation and Design (CAID), School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wei Zhang
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China
- Center for Alloy Innovation and Design (CAID), School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- Pazhou Lab, Pengcheng National Laboratory in Guangzhou, Guangzhou, 510320, China
| | - En Ma
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China
- Center for Alloy Innovation and Design (CAID), School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | - Riccardo Mazzarello
- Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, 52056, Germany
- JARA-FIT and JARA-HPC, RWTH Aachen University, Aachen, 52056, Germany
- Department of Physics, Sapienza University of Rome, Rome, 00185, Italy
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9
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Cecchi S, Lopez Garcia I, Mio AM, Zallo E, Abou El Kheir O, Calarco R, Bernasconi M, Nicotra G, Privitera SMS. Crystallization and Electrical Properties of Ge-Rich GeSbTe Alloys. NANOMATERIALS 2022; 12:nano12040631. [PMID: 35214960 PMCID: PMC8876497 DOI: 10.3390/nano12040631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/31/2022] [Accepted: 02/08/2022] [Indexed: 01/27/2023]
Abstract
Enrichment of GeSbTe alloys with germanium has been proposed as a valid approach to increase the crystallization temperature and therefore to address high-temperature applications of non-volatile phase change memories, such as embedded or automotive applications. However, the tendency of Ge-rich GeSbTe alloys to decompose with the segregation of pure Ge still calls for investigations on the basic mechanisms leading to element diffusion and compositional variations. With the purpose of identifying some possible routes to limit the Ge segregation, in this study, we investigate Ge-rich Sb2Te3 and Ge-rich Ge2Sb2Te5 with low (<40 at %) or high (>40 at %) amounts of Ge. The formation of the crystalline phases has been followed as a function of annealing temperature by X-ray diffraction. The temperature dependence of electrical properties has been evaluated by in situ resistance measurements upon annealing up to 300 °C. The segregation and decomposition processes have been studied by scanning transmission electron microscopy (STEM) and discussed on the basis of density functional theory calculations. Among the studied compositions, Ge-rich Ge2Sb2Te5 is found to be less prone to decompose with Ge segregation.
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Affiliation(s)
- Stefano Cecchi
- Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplatz 5–7, 10117 Berlin, Germany; (E.Z.); (R.C.)
- Department of Materials Science, University of Milano-Bicocca, via R. Cozzi 55, 20125 Milano, Italy; (O.A.E.K.); (M.B.)
- Correspondence:
| | - Iñaki Lopez Garcia
- Institute for Microelectronic and Microsystems (IMM), National Research Council (CNR), Zona Industriale Ottava Strada 5, 95121 Catania, Italy; (I.L.G.); (A.M.M.); (G.N.); (S.M.S.P.)
| | - Antonio M. Mio
- Institute for Microelectronic and Microsystems (IMM), National Research Council (CNR), Zona Industriale Ottava Strada 5, 95121 Catania, Italy; (I.L.G.); (A.M.M.); (G.N.); (S.M.S.P.)
| | - Eugenio Zallo
- Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplatz 5–7, 10117 Berlin, Germany; (E.Z.); (R.C.)
- Walter Schottky Institut, Physik Department, Technische Universität München, Am Coulombwall 4, 85748 Garching, Germany
| | - Omar Abou El Kheir
- Department of Materials Science, University of Milano-Bicocca, via R. Cozzi 55, 20125 Milano, Italy; (O.A.E.K.); (M.B.)
| | - Raffaella Calarco
- Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplatz 5–7, 10117 Berlin, Germany; (E.Z.); (R.C.)
- Institute for Microelectronic and Microsystems (IMM), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Roma, Italy
| | - Marco Bernasconi
- Department of Materials Science, University of Milano-Bicocca, via R. Cozzi 55, 20125 Milano, Italy; (O.A.E.K.); (M.B.)
| | - Giuseppe Nicotra
- Institute for Microelectronic and Microsystems (IMM), National Research Council (CNR), Zona Industriale Ottava Strada 5, 95121 Catania, Italy; (I.L.G.); (A.M.M.); (G.N.); (S.M.S.P.)
| | - Stefania M. S. Privitera
- Institute for Microelectronic and Microsystems (IMM), National Research Council (CNR), Zona Industriale Ottava Strada 5, 95121 Catania, Italy; (I.L.G.); (A.M.M.); (G.N.); (S.M.S.P.)
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10
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Zhou Y, Kirkpatrick W, Deringer VL. Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107515. [PMID: 34734441 DOI: 10.1002/adma.202107515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how this structure changes under pressure. The structural model so obtained contains abundant five-membered rings, as well as more complex seven- and eight-atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials.
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Affiliation(s)
- Yuxing Zhou
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | - William Kirkpatrick
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK
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11
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Dragoni D, Behler J, Bernasconi M. Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material. NANOSCALE 2021; 13:16146-16155. [PMID: 34542138 DOI: 10.1039/d1nr03432d] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Elemental antimony has been recently proposed as a promising material for phase change memories with improved performances with respect to the most used ternary chalcogenide alloys. The compositional simplification prevents reliability problems due to demixing of the alloy during memory operation. This is made possible by the dramatic stabilization of the amorphous phase once Sb is confined in an ultrathin film 3-5 nm thick. In this work, we shed light on the microscopic origin of this effect by means of large scale molecular dynamics simulations based on an interatomic potential generated with a machine learning technique. The simulations suggest that the dramatic reduction of the crystal growth velocity in the film with respect to the bulk is due to the effect of nanoconfinement on the fast β relaxation dynamics while the slow α relaxation is essentially unaffected.
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Affiliation(s)
- Daniele Dragoni
- Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy.
| | - Jörg Behler
- Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstr. 6, 37077 Göttingen, Germany
| | - Marco Bernasconi
- Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy.
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12
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Abou El Kheir O, Bernasconi M. High-Throughput Calculations on the Decomposition Reactions of Off-Stoichiometry GeSbTe Alloys for Embedded Memories. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:2382. [PMID: 34578698 PMCID: PMC8464663 DOI: 10.3390/nano11092382] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022]
Abstract
Chalcogenide GeSbTe (GST) alloys are exploited as phase change materials in a variety of applications ranging from electronic non-volatile memories to neuromorphic and photonic devices. In most applications, the prototypical Ge2Sb2Te5 compound along the GeTe-Sb2Te3 pseudobinary line is used. Ge-rich GST alloys, off the pseudobinary tie-line with a crystallization temperature higher than that of Ge2Sb2Te5, are currently explored for embedded phase-change memories of interest for automotive applications. During crystallization, Ge-rich GST alloys undergo a phase separation into pure Ge and less Ge-rich alloys. The detailed mechanisms underlying this transformation are, however, largely unknown. In this work, we performed high-throughput calculations based on Density Functional Theory (DFT) to uncover the most favorable decomposition pathways of Ge-rich GST alloys. The knowledge of the DFT formation energy of all GST alloys in the central part of the Ge-Sb-Te ternary phase diagram allowed us to identify the cubic crystalline phases that are more likely to form during the crystallization of a generic GST alloy. This scheme is exemplified by drawing a decomposition map for alloys on the Ge-Ge1Sb2Te4 tie-line. A map of decomposition propensity is also constructed, which suggests a possible strategy to minimize phase separation by still keeping a high crystallization temperature.
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Affiliation(s)
| | - Marco Bernasconi
- Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy;
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13
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Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian Process Regression for Materials and Molecules. Chem Rev 2021; 121:10073-10141. [PMID: 34398616 PMCID: PMC8391963 DOI: 10.1021/acs.chemrev.1c00022] [Citation(s) in RCA: 229] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Indexed: 12/18/2022]
Abstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
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Affiliation(s)
- Volker L. Deringer
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
| | - Albert P. Bartók
- Department
of Physics and Warwick Centre for Predictive Modelling, School of
Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Noam Bernstein
- Center
for Computational Materials Science, U.S.
Naval Research Laboratory, Washington D.C. 20375, United States
| | - David M. Wilkins
- Atomistic
Simulation Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| | - Michele Ceriotti
- Laboratory
of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale
de Lausanne, Lausanne, Switzerland
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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14
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Young TA, Johnston-Wood T, Deringer VL, Duarte F. A transferable active-learning strategy for reactive molecular force fields. Chem Sci 2021; 12:10944-10955. [PMID: 34476072 PMCID: PMC8372546 DOI: 10.1039/d1sc01825f] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/04/2021] [Indexed: 11/25/2022] Open
Abstract
Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but doing so typically requires considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models can be developed for diverse chemical systems in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations on a reference potential-energy surface. The approach uses separate intra- and inter-molecular fits and employs a prospective error metric to assess the accuracy of the potentials. We demonstrate applications to a range of molecular systems with relevance to computational organic chemistry: ranging from bulk solvents, a solvated metal ion and a metallocage onwards to chemical reactivity, including a bifurcating Diels-Alder reaction in the gas phase and non-equilibrium dynamics (a model SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for reactive molecular systems.
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Affiliation(s)
- Tom A Young
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Tristan Johnston-Wood
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford Oxford OX1 3QR UK
| | - Fernanda Duarte
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
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15
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Blow KE, Quigley D, Sosso GC. The seven deadly sins: When computing crystal nucleation rates, the devil is in the details. J Chem Phys 2021; 155:040901. [PMID: 34340373 DOI: 10.1063/5.0055248] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The formation of crystals has proven to be one of the most challenging phase transformations to quantitatively model-let alone to actually understand-be it by means of the latest experimental technique or the full arsenal of enhanced sampling approaches at our disposal. One of the most crucial quantities involved with the crystallization process is the nucleation rate, a single elusive number that is supposed to quantify the average probability for a nucleus of critical size to occur within a certain volume and time span. A substantial amount of effort has been devoted to attempt a connection between the crystal nucleation rates computed by means of atomistic simulations and their experimentally measured counterparts. Sadly, this endeavor almost invariably fails to some extent, with the venerable classical nucleation theory typically blamed as the main culprit. Here, we review some of the recent advances in the field, focusing on a number of perhaps more subtle details that are sometimes overlooked when computing nucleation rates. We believe it is important for the community to be aware of the full impact of aspects, such as finite size effects and slow dynamics, that often introduce inconspicuous and yet non-negligible sources of uncertainty into our simulations. In fact, it is key to obtain robust and reproducible trends to be leveraged so as to shed new light on the kinetics of a process, that of crystal nucleation, which is involved into countless practical applications, from the formulation of pharmaceutical drugs to the manufacturing of nano-electronic devices.
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Affiliation(s)
- Katarina E Blow
- Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - David Quigley
- Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Gabriele C Sosso
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
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16
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Morawietz T, Artrith N. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications. J Comput Aided Mol Des 2021; 35:557-586. [PMID: 33034008 PMCID: PMC8018928 DOI: 10.1007/s10822-020-00346-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/26/2020] [Indexed: 01/13/2023]
Abstract
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.
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Affiliation(s)
- Tobias Morawietz
- Bayer AG, Pharmaceuticals, R&D, Digital Technologies, Computational Molecular Design, 42096 Wuppertal, Germany
| | - Nongnuch Artrith
- Department of Chemical Engineering, Columbia University, New York, NY 10027 USA
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17
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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18
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Hoffmann MM, Too MD, Vogel M, Gutmann T, Buntkowsky G. Breakdown of the Stokes-Einstein Equation for Solutions of Water in Oil Reverse Micelles. J Phys Chem B 2020; 124:9115-9125. [PMID: 32924487 DOI: 10.1021/acs.jpcb.0c06124] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An experimental study is presented for the reverse micellar system of 15% by mass polydisperse hexaethylene glycol monodecylether (C10E6) in cyclohexane with varying amounts of added water up to 4% by mass. Measurements of viscosity and self-diffusion coefficients were taken as a function of temperature between 10 and 45 °C at varying sample water loads but fixed C10E6/cyclohexane composition. The results were used to inspect the validity of the Stokes-Einstein equation for this system. Unreasonably small reverse average micelle radii and aggregation numbers were obtained with the Stokes-Einstein equation, but reasonable values for these quantities were obtained using the ratio of surfactant-to-cyclohexane self-diffusion coefficients. While bulk viscosity increased with increasing water load, a concurrent expected decrease of self-diffusion coefficient was only observed for the surfactant and water but not for cyclohexane, which showed independence of water load. Moreover, a spread of self-diffusion coefficients was observed for the protons associated with the ethylene oxide repeat unit in samples with polydisperse C10E6 but not in a sample with monodisperse C10E6. These findings were interpreted by the presence of reverse micelle to reverse micelle hopping motions that with higher water load become increasingly selective toward C10E6 molecules with short ethylene oxide repeat units, while those with long ethylene oxide repeat units remain trapped within the reverse micelle because of the increased hydrogen bonding interactions with the water inside the growing core of the reverse micelle. Despite the observed breakdown of the Stokes-Einstein equation, the temperature dependence of the viscosities and self-diffusion coefficients was found to follow Arrhenius behavior over the investigated range of temperatures.
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Affiliation(s)
- Markus M Hoffmann
- Department of Chemistry and Biochemistry, State University of New York College at Brockport, Brockport, New York 14420, United States
| | - Matthew D Too
- Department of Chemistry and Biochemistry, State University of New York College at Brockport, Brockport, New York 14420, United States
| | - Michael Vogel
- Institute of Condensed Matter Physics, Technical University Darmstadt, Hochschulstraße 6, Darmstadt 64289, Germany
| | - Torsten Gutmann
- Institute of Physical Chemistry, Technical University Darmstadt, Alarich-Weiss-Straße 8, Darmstadt D-64287, Germany
| | - Gerd Buntkowsky
- Institute of Physical Chemistry, Technical University Darmstadt, Alarich-Weiss-Straße 8, Darmstadt D-64287, Germany
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19
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Choi YJ, Jhi SH. Efficient Training of Machine Learning Potentials by a Randomized Atomic-System Generator. J Phys Chem B 2020; 124:8704-8710. [PMID: 32910653 DOI: 10.1021/acs.jpcb.0c05075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning potentials provide an efficient and comprehensive tool to simulate large-scale systems inaccessible by conventional first-principles methods still in a similar level of accuracy. One critical issue in constructing machine learning potentials is to build training data sets cost-effectively that can represent the potential energy surface in a wide range of configurations. We develop a scheme named randomized atomic-system generator (RAG) to produce the training sets that widely cover the potential energy surface by combining the random sampling and structural optimization. We apply the scheme to construct the machine learning potentials for simulation of chalcogen-based phase change materials. Constructed machine learning potentials successfully simulate the dynamics of melting and crystallization processes of binary GeTe at a level comparable to first-principles simulations. The visual analysis shows that the RAG-generated training set represents the crystallization process including the amorphous phases. From the velocity autocorrelation function obtained from the molecular dynamics simulations, we calculate the phonon density of states to analyze the vibrational properties during crystallization.
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Affiliation(s)
- Young-Jae Choi
- Department of Physics, POSTECH, Cheongam-ro 77, Pohang 37673, Republic of Korea
| | - Seung-Hoon Jhi
- Department of Physics, POSTECH, Cheongam-ro 77, Pohang 37673, Republic of Korea
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20
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Jinnouchi R, Karsai F, Verdi C, Asahi R, Kresse G. Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials. J Chem Phys 2020; 152:234102. [DOI: 10.1063/5.0009491] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Ryosuke Jinnouchi
- Computational Materials Physics, Faculty of Physics, University of Vienna, Sensengasse 8/16, 1090 Vienna, Austria
| | - Ferenc Karsai
- VASP Software GmbH, Sensengasse 8, 1090 Vienna, Austria
| | - Carla Verdi
- Computational Materials Physics, Faculty of Physics, University of Vienna, Sensengasse 8/16, 1090 Vienna, Austria
| | - Ryoji Asahi
- Toyota Central Research and Developments Laboratories, Inc., Aichi 480-1192, Japan
| | - Georg Kresse
- Computational Materials Physics, Faculty of Physics, University of Vienna, Sensengasse 8/16, 1090 Vienna, Austria
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21
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Abstract
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.
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22
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Deringer VL, Caro MA, Csányi G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1902765. [PMID: 31486179 DOI: 10.1002/adma.201902765] [Citation(s) in RCA: 202] [Impact Index Per Article: 40.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/26/2019] [Indexed: 05/22/2023]
Abstract
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 materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research.
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Affiliation(s)
- Volker L Deringer
- Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Miguel A Caro
- Department of Electrical Engineering and Automation and Department of Applied Physics, Aalto University, Espoo, 02150, Finland
| | - Gábor Csányi
- Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
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23
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A Study of the Shock Sensitivity of Energetic Single Crystals by Large-Scale Ab Initio Molecular Dynamics Simulations. NANOMATERIALS 2019; 9:nano9091251. [PMID: 31484358 PMCID: PMC6780424 DOI: 10.3390/nano9091251] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 08/22/2019] [Accepted: 08/27/2019] [Indexed: 11/17/2022]
Abstract
Understanding the reaction initiation of energetic single crystals under external stimuli is a long-term challenge in the field of high energy density materials. Herewith, we developed an ab initio molecular dynamics method based on the multiscale shock technique (MSST) and reported the reaction initiation mechanism by performing large-scale simulations for the sensitive explosive benzotrifuroxan (BTF), insensitive explosive triaminotrinitrobenzene (TATB), four polymorphs of hexanitrohexaazaisowurtzitane (CL-20) pristine crystals and five novel CL-20 cocrystals. A theoretical indicator, tinitiation, the delay of decomposition reaction under shock, was proposed to characterize the shock sensitivity of energetic single crystal, which was proved to be reliable and satisfactorily consistent with experiments. We found that it was the coupling of heat and pressure that drove the shock reaction, wherein the vibrational spectra, the specific heat capacity, as well as the strength of the trigger bonds were the determinants of the shock sensitivity. The intermolecular hydrogen bonds were found to effectively buffer the system from heating, thereby delaying the decomposition reaction and reducing the shock sensitivity of the energetic single crystal. Theoretical rules for synthesizing novel energetic materials with low shock sensitivity were given. Our work is expected to provide a useful reference for the understanding, certifying and adjusting of the shock sensitivity of novel energetic materials.
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24
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Bernasconi M. Atomistic Simulations of Phase Change Materials for Electronic Memories. INTERNATIONAL JOURNAL OF NANOSCIENCE 2019. [DOI: 10.1142/s0219581x19400829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We review our results on large-scale atomistic simulations of the phase change compound GeTe of interest for applications in nonvolatile electronic memories. The simulations are based on an interatomic potential with an accuracy close to that of the density functional theory (DFT). The potential was generated by fitting a DFT database by means of an artificial neural network method. This methodological advance allowed us to perform molecular dynamics simulations with several thousand atoms for several ns that provided useful insights on several properties of interest for the operation of phase change memories, including the crystallization kinetics, the dynamics of the supercooled liquid, the structural relaxation in the glass and the properties of nanowires.
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Affiliation(s)
- M. Bernasconi
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, Milano, Italy
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25
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Bernstein N, Bhattarai B, Csányi G, Drabold DA, Elliott SR, Deringer VL. Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon. Angew Chem Int Ed Engl 2019; 58:7057-7061. [PMID: 30835962 PMCID: PMC6563111 DOI: 10.1002/anie.201902625] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Indexed: 11/29/2022]
Abstract
Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 K s-1 . Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.
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Affiliation(s)
- Noam Bernstein
- Center for Materials Physics and TechnologyU.S. Naval Research LaboratoryWashingtonDC20375USA
| | - Bishal Bhattarai
- Department of Physics and AstronomyOhio UniversityAthensOH45701USA
| | - Gábor Csányi
- Department of EngineeringUniversity of CambridgeCambridgeCB2 1PZUK
| | - David A. Drabold
- Department of Physics and AstronomyOhio UniversityAthensOH45701USA
| | | | - Volker L. Deringer
- Department of EngineeringUniversity of CambridgeCambridgeCB2 1PZUK
- Department of ChemistryUniversity of CambridgeCambridgeCB2 1EWUK
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26
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Bernstein N, Bhattarai B, Csányi G, Drabold DA, Elliott SR, Deringer VL. Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201902625] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Noam Bernstein
- Center for Materials Physics and Technology U.S. Naval Research Laboratory Washington DC 20375 USA
| | - Bishal Bhattarai
- Department of Physics and Astronomy Ohio University Athens OH 45701 USA
| | - Gábor Csányi
- Department of Engineering University of Cambridge Cambridge CB2 1PZ UK
| | - David A. Drabold
- Department of Physics and Astronomy Ohio University Athens OH 45701 USA
| | | | - Volker L. Deringer
- Department of Engineering University of Cambridge Cambridge CB2 1PZ UK
- Department of Chemistry University of Cambridge Cambridge CB2 1EW UK
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27
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Gabardi S, Sosso GG, Behler J, Bernasconi M. Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations. Faraday Discuss 2019; 213:287-301. [PMID: 30379974 DOI: 10.1039/c8fd00101d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Strategies to reduce the incubation time for crystal nucleation and thus the stochasticity of the set process are of relevance for the operation of phase change memories in ultra-scaled geometries. With these premises, in this work we investigate the crystallization kinetics of the phase change compound GeTe. We have performed large scale molecular dynamics simulations using an interatomic potential, generated previously from a neural network fitting of a database of ab initio energies. We have addressed the crystallization of models of amorphous GeTe annealed at different temperatures above the glass transition. The results on the distribution of subcritical nuclei and on the crystal growth velocity of postcritical ones are compared with our previous simulations of the supercooled liquid quenched from the melt. We find that a large population of subcritical nuclei can form at the lower temperatures where the nucleation rate is large. This population partially survives upon fast annealing, which leads to a dramatic reduction of the incubation time at high temperatures where the crystal growth velocity is maximal. This priming effect could be exploited to enhance the speed of the set process in phase change memories.
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Affiliation(s)
- Silvia Gabardi
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy.
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28
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Yarema O, Perevedentsev A, Ovuka V, Baade P, Volk S, Wood V, Yarema M. Colloidal Phase-Change Materials: Synthesis of Monodisperse GeTe Nanoparticles and Quantification of Their Size-Dependent Crystallization. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2018; 30:6134-6143. [PMID: 30270986 DOI: 10.1021/acs.chemmater.7b04710] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/19/2018] [Indexed: 05/28/2023]
Abstract
Phase-change memory materials refer to a class of materials that can exist in amorphous and crystalline phases with distinctly different electrical or optical properties, as well as exhibit outstanding crystallization kinetics and optimal phase transition temperatures. This paper focuses on the potential of colloids as phase-change memory materials. We report a novel synthesis for amorphous GeTe nanoparticles based on an amide-promoted approach that enables accurate size control of GeTe nanoparticles between 4 and 9 nm, narrow size distributions down to 9-10%, and synthesis upscaling to reach multigram chemical yields per batch. We then quantify the crystallization phase transition for GeTe nanoparticles, employing high-temperature X-ray diffraction, differential scanning calorimetry, and transmission electron microscopy. We show that GeTe nanoparticles crystallize at higher temperatures than the bulk GeTe material and that crystallization temperature increases with decreasing size. We can explain this size-dependence using the entropy of crystallization model and classical nucleation theory. The size-dependences quantified here highlight possible benefits of nanoparticles for phase-change memory applications.
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Affiliation(s)
- Olesya Yarema
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Aleksandr Perevedentsev
- Polymer Technology, Department of Materials, ETH Zurich, Vladimir-Prelog-Weg 5, CH-8093 Zurich, Switzerland
| | - Vladimir Ovuka
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Paul Baade
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Sebastian Volk
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Vanessa Wood
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Maksym Yarema
- Materials and Device Engineering Group, Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
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29
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Yarema O, Perevedentsev A, Ovuka V, Baade P, Volk S, Wood V, Yarema M. Colloidal Phase-Change Materials: Synthesis of Monodisperse GeTe Nanoparticles and Quantification of Their Size-Dependent Crystallization. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2018; 30:6134-6143. [PMID: 30270986 PMCID: PMC6156088 DOI: 10.1021/acs.chemmater.8b02702] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/19/2018] [Indexed: 05/31/2023]
Abstract
Phase-change memory materials refer to a class of materials that can exist in amorphous and crystalline phases with distinctly different electrical or optical properties, as well as exhibit outstanding crystallization kinetics and optimal phase transition temperatures. This paper focuses on the potential of colloids as phase-change memory materials. We report a novel synthesis for amorphous GeTe nanoparticles based on an amide-promoted approach that enables accurate size control of GeTe nanoparticles between 4 and 9 nm, narrow size distributions down to 9-10%, and synthesis upscaling to reach multigram chemical yields per batch. We then quantify the crystallization phase transition for GeTe nanoparticles, employing high-temperature X-ray diffraction, differential scanning calorimetry, and transmission electron microscopy. We show that GeTe nanoparticles crystallize at higher temperatures than the bulk GeTe material and that crystallization temperature increases with decreasing size. We can explain this size-dependence using the entropy of crystallization model and classical nucleation theory. The size-dependences quantified here highlight possible benefits of nanoparticles for phase-change memory applications.
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Affiliation(s)
- Olesya Yarema
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Aleksandr Perevedentsev
- Polymer
Technology, Department of Materials, ETH
Zurich, Vladimir-Prelog-Weg 5, CH-8093 Zurich, Switzerland
| | - Vladimir Ovuka
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Paul Baade
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Sebastian Volk
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Vanessa Wood
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
| | - Maksym Yarema
- Materials
and Device Engineering Group, Department of Information Technology
and Electrical Engineering, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland
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30
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Mocanu FC, Konstantinou K, Lee TH, Bernstein N, Deringer VL, Csányi G, Elliott SR. Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential. J Phys Chem B 2018; 122:8998-9006. [DOI: 10.1021/acs.jpcb.8b06476] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Felix C. Mocanu
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | | | - Tae Hoon Lee
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Noam Bernstein
- Center for Materials Physics and Technology, U.S. Naval Research Laboratory, Washington, District of Columbia 20375, United States
| | - Volker L. Deringer
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Stephen R. Elliott
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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31
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Fujikake S, Deringer VL, Lee TH, Krynski M, Elliott SR, Csányi G. Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures. J Chem Phys 2018; 148:241714. [DOI: 10.1063/1.5016317] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- So Fujikake
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- École des Ponts ParisTech, F-77455 Marne-la-Vallée Cedex 2, France
- Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Volker L. Deringer
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Tae Hoon Lee
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Marcin Krynski
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Stephen R. Elliott
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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32
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Deringer VL, Bernstein N, Bartók AP, Cliffe MJ, Kerber RN, Marbella LE, Grey CP, Elliott SR, Csányi G. Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics. J Phys Chem Lett 2018; 9:2879-2885. [PMID: 29754489 DOI: 10.1021/acs.jpclett.8b00902] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Amorphous silicon ( a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials.
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Affiliation(s)
- Volker L Deringer
- Department of Engineering , University of Cambridge , Cambridge CB2 1PZ , United Kingdom
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Noam Bernstein
- Center for Materials Physics and Technology , U.S. Naval Research Laboratory , Washington , District of Columbia 20375 , United States
| | - Albert P Bartók
- Scientific Computing Department, Science and Technology Facilities Council , Rutherford Appleton Laboratory , Oxfordshire OX11 0QX , United Kingdom
| | - Matthew J Cliffe
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Rachel N Kerber
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Lauren E Marbella
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Clare P Grey
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Stephen R Elliott
- Department of Chemistry , University of Cambridge , Cambridge CB2 1EW , United Kingdom
| | - Gábor Csányi
- Department of Engineering , University of Cambridge , Cambridge CB2 1PZ , United Kingdom
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33
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Caro MA, Deringer VL, Koskinen J, Laurila T, Csányi G. Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon. PHYSICAL REVIEW LETTERS 2018; 120:166101. [PMID: 29756912 DOI: 10.1103/physrevlett.120.166101] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Indexed: 05/13/2023]
Abstract
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp^{3} fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp^{3} content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp^{3} fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power.
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Affiliation(s)
- Miguel A Caro
- Department of Electrical Engineering and Automation, Aalto University, Espoo 02150, Finland
- Department of Applied Physics, Aalto University, Espoo 02150, Finland
| | - Volker L Deringer
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jari Koskinen
- Department of Chemistry and Materials Science, Aalto University, Espoo 02150, Finland
| | - Tomi Laurila
- Department of Electrical Engineering and Automation, Aalto University, Espoo 02150, Finland
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
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34
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Sosso GC, Deringer VL, Elliott SR, Csányi G. Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials. MOLECULAR SIMULATION 2018. [DOI: 10.1080/08927022.2018.1447107] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gabriele C. Sosso
- Department of Chemistry and Centre for Scientific Computing, University of Warwick , Coventry, UK
| | - Volker L. Deringer
- Department of Engineering, University of Cambridge , Cambridge, UK
- Department of Chemistry, University of Cambridge , Cambridge, UK
| | | | - Gábor Csányi
- Department of Engineering, University of Cambridge , Cambridge, UK
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35
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Chen B, de Wal D, ten Brink GH, Palasantzas G, Kooi BJ. Resolving Crystallization Kinetics of GeTe Phase-Change Nanoparticles by Ultrafast Calorimetry. CRYSTAL GROWTH & DESIGN 2018; 18:1041-1046. [PMID: 29445317 PMCID: PMC5806086 DOI: 10.1021/acs.cgd.7b01498] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Indexed: 06/01/2023]
Abstract
Chalcogenide-based phase change materials (PCMs) are promising candidates for the active element in novel electrical nonvolatile memories and have been applied successfully in rewritable optical disks. Nanostructured PCMs are considered as the next generation building blocks for their low power consumption, high storage density, and fast switching speed. Yet their crystallization kinetics at high temperature, the rate-limiting property upon switching, faces great challenges due to the short time and length scales involved. Here we present a facile method to synthesize highly controlled, ligand-free GeTe nanoparticles, an important PCM, with an average diameter under 10 nm. Subsequent crystallization by slow and ultrafast rates allows unravelling of the crystallization kinetics, demonstrating the breakdown of Arrhenius behavior for the crystallization rate and a fragile-to-strong transition in the viscosity as well as the overall crystal growth rate for the as-deposited GeTe nanoparticles. The obtained results pave the way for further development of phase-change memory based on GeTe with sub-lithographic sizes.
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Affiliation(s)
- Bin Chen
- Zernike Institute for Advanced
Materials,
Univerisity of Grnoingen, Nijenborgh 4, 9747
AG, Groningen, The Netherlands
| | - Dennis de Wal
- Zernike Institute for Advanced
Materials,
Univerisity of Grnoingen, Nijenborgh 4, 9747
AG, Groningen, The Netherlands
| | - Gert H. ten Brink
- Zernike Institute for Advanced
Materials,
Univerisity of Grnoingen, Nijenborgh 4, 9747
AG, Groningen, The Netherlands
| | - George Palasantzas
- Zernike Institute for Advanced
Materials,
Univerisity of Grnoingen, Nijenborgh 4, 9747
AG, Groningen, The Netherlands
| | - Bart J. Kooi
- Zernike Institute for Advanced
Materials,
Univerisity of Grnoingen, Nijenborgh 4, 9747
AG, Groningen, The Netherlands
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36
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Hellström M, Behler J. Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations. Phys Chem Chem Phys 2018; 19:82-96. [PMID: 27805193 DOI: 10.1039/c6cp06547c] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Sodium hydroxide, NaOH, is one of the most widely-used chemical reagents, but the structural properties of its aqueous solutions have only sparingly been characterized. Here, we automatically classify the cation coordination polyhedra obtained from molecular dynamics simulations. We find that, for example, with increasing concentration, octahedral coordination geometries become less favored, while the opposite is true for the trigonal prism. At high concentrations, the coordination polyhedra frequently deviate considerably from "ideal" polyhedra, because of an increased extent of interligand hydrogen-bonding, in which hydrogen bonds between two ligands, either OH2 or OH-, around the same Na+ are formed. In saturated solutions, with concentrations of about 19 mol L-1, ligands are frequently shared between multiple Na+ ions as a result of the deficiency of solvent molecules. This results in more complex structural patterns involving certain "characteristic" polyhedron connectivities, such as octahedra sharing ligands with capped trigonal prisms, and tetrahedra sharing ligands with trigonal bipyramids. The simulations were performed using a density-functional-theory-based reactive high-dimensional neural network potential, that was extensively validated against available neutron and X-ray diffraction data from the literature.
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Affiliation(s)
- Matti Hellström
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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37
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Rao F, Ding K, Zhou Y, Zheng Y, Xia M, Lv S, Song Z, Feng S, Ronneberger I, Mazzarello R, Zhang W, Ma E. Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing. Science 2017; 358:1423-1427. [PMID: 29123020 DOI: 10.1126/science.aao3212] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/30/2017] [Indexed: 01/26/2023]
Abstract
Operation speed is a key challenge in phase-change random-access memory (PCRAM) technology, especially for achieving subnanosecond high-speed cache memory. Commercialized PCRAM products are limited by the tens of nanoseconds writing speed, originating from the stochastic crystal nucleation during the crystallization of amorphous germanium antimony telluride (Ge2Sb2Te5). Here, we demonstrate an alloying strategy to speed up the crystallization kinetics. The scandium antimony telluride (Sc0.2Sb2Te3) compound that we designed allows a writing speed of only 700 picoseconds without preprogramming in a large conventional PCRAM device. This ultrafast crystallization stems from the reduced stochasticity of nucleation through geometrically matched and robust scandium telluride (ScTe) chemical bonds that stabilize crystal precursors in the amorphous state. Controlling nucleation through alloy design paves the way for the development of cache-type PCRAM technology to boost the working efficiency of computing systems.
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Affiliation(s)
- Feng Rao
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.,College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Keyuan Ding
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.,College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Yuxing Zhou
- Center for Advancing Materials Performance from the Nanoscale, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yonghui Zheng
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Mengjiao Xia
- International Laboratory of Quantum Functional Materials of Henan, School of Physics and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Shilong Lv
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Zhitang Song
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
| | - Songlin Feng
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Ider Ronneberger
- Institute for Theoretical Solid State Physics, JARA-FIT and JARA-HPC, RWTH Aachen University, Aachen D-52074, Germany
| | - Riccardo Mazzarello
- Institute for Theoretical Solid State Physics, JARA-FIT and JARA-HPC, RWTH Aachen University, Aachen D-52074, Germany
| | - Wei Zhang
- Center for Advancing Materials Performance from the Nanoscale, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China
| | - Evan Ma
- Center for Advancing Materials Performance from the Nanoscale, State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.,Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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38
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Behler J. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. Angew Chem Int Ed Engl 2017; 56:12828-12840. [PMID: 28520235 DOI: 10.1002/anie.201703114] [Citation(s) in RCA: 323] [Impact Index Per Article: 46.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Indexed: 11/06/2022]
Abstract
Modern simulation techniques have reached a level of maturity which allows a wide range of problems in chemistry and materials science to be addressed. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and despite the rapid evolution of computer hardware no fundamental change in this situation can be expected. Consequently, the development of more efficient but equally reliable atomistic potentials to reach an atomic level understanding of complex systems has received considerable attention in recent years. A promising new development has been the introduction of machine learning (ML) methods to describe the atomic interactions. Once trained with electronic structure data, ML potentials can accelerate computer simulations by several orders of magnitude, while preserving quantum mechanical accuracy. This Review considers the methodology of an important class of ML potentials that employs artificial neural networks.
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstrasse 6, 37077, Göttingen, Germany
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39
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Behler J. Hochdimensionale neuronale Netze für Potentialhyperflächen großer molekularer und kondensierter Systeme. Angew Chem Int Ed Engl 2017. [DOI: 10.1002/ange.201703114] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jörg Behler
- Universität Göttingen; Institut für Physikalische Chemie, Theoretische Chemie; Tammannstraße 6 37077 Göttingen Deutschland
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40
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Tribello GA, Giberti F, Sosso GC, Salvalaglio M, Parrinello M. Analyzing and Driving Cluster Formation in Atomistic Simulations. J Chem Theory Comput 2017; 13:1317-1327. [DOI: 10.1021/acs.jctc.6b01073] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Gareth A. Tribello
- Atomistic
Simulation Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, United Kingdom
| | - Federico Giberti
- Computational
Science, Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Via Giuseppe Buffi 13, C-6900 Lugano, Switzerland
| | - Gabriele C. Sosso
- Thomas
Young Centre, London Centre for Nanotechnology and Department of Physics
and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Matteo Salvalaglio
- Department
of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Michele Parrinello
- Computational
Science, Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Via Giuseppe Buffi 13, C-6900 Lugano, Switzerland
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41
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Sosso GC, Caravati S, Rotskoff G, Vaikuntanathan S, Hassanali A. On the Role of Nonspherical Cavities in Short Length-Scale Density Fluctuations in Water. J Phys Chem A 2016; 121:370-380. [PMID: 27935707 DOI: 10.1021/acs.jpca.6b11168] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Density fluctuations in liquid water are at the heart of numerous phenomena associated with hydrophobic effects such as protein folding and the interaction between biomolecules. One of the most fundamental processes in this regard is the solvation of hydrophobic solutes in water. The vast majority of theoretical and numerical studies examine density fluctuations at the short length scale focusing exclusively on spherical cavities. In this work, we use both first-principles and classical molecular dynamics simulations to demonstrate that density fluctuations in liquid water can deviate significantly from the canonical spherical shapes. We show that regions of empty space are frequently characterized by exotic, highly asymmetric shapes that can be quite delocalized over the hydrogen bond network. Interestingly, density fluctuations of these shapes are characterized by Gaussian statistics with larger fluctuations. An important consequence of this is that the work required to create non spherical cavities can be substantially smaller than that of spheres. This feature is also qualitatively captured by the Lum-Chandler-Weeks theory. The scaling behavior of the free energy as a function of the volume at short length scales is qualitatively different for the nonspherical entities. We also demonstrate that nonspherical density fluctuations are important for accommodating the hydrophobic amino acid alanine and are thus likely to have significant implications when it comes to solvating highly asymmetrical species such as alkanes, polymers, or biomolecules.
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Affiliation(s)
- Gabriele Cesare Sosso
- Thomas Young Centre, London Centre for Nanotechnology and Department of Physics and Astronomy, University College London , Gower Street, London WC1E 6BT, United Kingdom
| | - Sebastiano Caravati
- Department of Chemistry, University of Zurich , Winterhurerstrasse 190, Zurich CH-8057, Switzerland
| | - Grant Rotskoff
- Biophysics Graduate Group, University of California , Berkeley, California 94720, United States
| | | | - Ali Hassanali
- Condensed Matter and Statistical Physics Section, The Abdus Salam International Centre for Theoretical Physics , I-34151 Trieste, Italy
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42
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Zhou X, Du Y, Behera JK, Wu L, Song Z, Simpson RE. Oxygen Tuned Local Structure and Phase-Change Performance of Germanium Telluride. ACS APPLIED MATERIALS & INTERFACES 2016; 8:20185-20191. [PMID: 27430363 DOI: 10.1021/acsami.6b05071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The effect of oxygen on the local structure of Ge atoms in GeTe-O materials has been investigated. Oxygen leads to a significant modification to the vibrational modes of Ge octahedra, which results from a decrease in its coordination. We find that a defective octahedral Ge network is the crucial fingerprint for rapid and reversible structural transitions in GeTe-based phase change materials. The appearance of oxide Raman modes confirms phase separation into GeO and TeO at high level O doping. Counterintuitively, despite the increase in crystallization temperature of oxygen doped GeTe-O phase change materials, when GeTe-O materials are used in electrical phase change memory cells, the electrical switching energy is lower than the pure GeTe material. This switching energy reduction is ascribed to the smaller change in volume, and therefore smaller enthalpy change, for the oxygen doped GeTe materials.
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Affiliation(s)
- Xilin Zhou
- ACTA Lab, Singapore University of Technology and Design , 8 Somapah Road, 487372, Singapore
| | - Yonghua Du
- Institute of Chemical and Engineering Sciences, A*STAR , 1 Pesek Road, Jurong Island 627833, Singapore
| | - Jitendra K Behera
- ACTA Lab, Singapore University of Technology and Design , 8 Somapah Road, 487372, Singapore
| | - Liangcai Wu
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences , 200050 Shanghai, China
| | - Zhitang Song
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences , 200050 Shanghai, China
| | - Robert E Simpson
- ACTA Lab, Singapore University of Technology and Design , 8 Somapah Road, 487372, Singapore
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43
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Two-step crystal growth mechanism during crystallization of an undercooled Ni50Al50 alloy. Sci Rep 2016; 6:31062. [PMID: 27486073 PMCID: PMC4971477 DOI: 10.1038/srep31062] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 07/12/2016] [Indexed: 11/10/2022] Open
Abstract
Crystallization processes are always accompanied by the emergence of multiple intermediate states, of which the structures and transition dynamics are far from clarity, since it is difficult to experimentally observe the microscopic pathway. To insight the structural evolution and the crystallization dynamics, we perform large-scale molecular dynamics simulations to investigate the time-dependent crystallization behavior of the NiAl intermetallic upon rapid solidification. The simulation results reveal that the crystallization process occurs via a two-step growth mechanism, involving the formation of initial non-equilibrium long range order (NLRO) regions and of the subsequent equilibrium long range order (ELRO) regions. The formation of the NLRO regions makes the grains rather inhomogeneous, while the rearrangement of the NLRO regions into the ELRO regions makes the grains more ordered and compact. This two-step growth mechanism is actually controlled by the evolution of the coordination polyhedra, which are characterized predominantly by the transformation from five-fold symmetry to four-fold and six-fold symmetry. From liquids to NLRO and further to ELRO, the five-fold symmetry of these polyhedra gradually fades, and finally vanishes when B2 structure is distributed throughout the grain bulk. The energy decrease along the pathway further implies the reliability of the proposed crystallization processes.
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44
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Sosso G, Chen J, Cox SJ, Fitzner M, Pedevilla P, Zen A, Michaelides A. Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations. Chem Rev 2016; 116:7078-116. [PMID: 27228560 PMCID: PMC4919765 DOI: 10.1021/acs.chemrev.5b00744] [Citation(s) in RCA: 379] [Impact Index Per Article: 47.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Indexed: 11/28/2022]
Abstract
The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insights into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that, in the past few decades, have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state-of-the-art computational methods by reviewing simulations of such processes as ice nucleation and the crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insights into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that, as a result, the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that, by improving (i) existing interatomic potentials and (ii) currently available enhanced sampling methods, the community can move toward accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments.
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Affiliation(s)
- Gabriele
C. Sosso
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
| | - Ji Chen
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
| | | | - Martin Fitzner
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
| | - Philipp Pedevilla
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
| | - Andrea Zen
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
| | - Angelos Michaelides
- Thomas Young Centre, London
Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street WC1E
6BT London, U.K.
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45
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Giberti F, Salvalaglio M, Parrinello M. Metadynamics studies of crystal nucleation. IUCRJ 2015; 2:256-66. [PMID: 25866662 PMCID: PMC4392418 DOI: 10.1107/s2052252514027626] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 12/18/2014] [Indexed: 05/14/2023]
Abstract
Crystallization processes are characterized by activated events and long timescales. These characteristics prevent standard molecular dynamics techniques from being efficiently used for the direct investigation of processes such as nucleation. This short review provides an overview on the use of metadynamics, a state-of-the-art enhanced sampling technique, for the simulation of phase transitions involving the production of a crystalline solid. In particular the principles of metadynamics are outlined, several order parameters are described that have been or could be used in conjunction with metadynamics to sample nucleation events and then an overview is given of recent metadynamics results in the field of crystal nucleation.
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Affiliation(s)
- Federico Giberti
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Matteo Salvalaglio
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8092 Zurich, Switzerland
- Facoltá di informatica, Istituto di Scienze Computazionali, Universitá della Svizzera Italiana, CH-6900 Lugano, Switzerland
- ETH Zurich, Institute of Process Engineering, Soneggstrasse 3, CH-8092 Zurich, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8092 Zurich, Switzerland
- Facoltá di informatica, Istituto di Scienze Computazionali, Universitá della Svizzera Italiana, CH-6900 Lugano, Switzerland
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46
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Li Y, Li J, Liu B. The atomic-scale nucleation mechanism of NiTi metallic glasses upon isothermal annealing studied via molecular dynamics simulations. Phys Chem Chem Phys 2015; 17:27127-35. [DOI: 10.1039/c5cp04040j] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The nucleation of devitrified metallic glasses is induced either by the inherited ordered atoms or by the nucleus precursor evolved directly from the liquid.
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Affiliation(s)
- Yang Li
- Key Laboratory of Advanced Materials (MOE)
- School of Materials Science and Engineering
- Tsinghua University
- Beijing 100084
- China
| | - JiaHao Li
- Key Laboratory of Advanced Materials (MOE)
- School of Materials Science and Engineering
- Tsinghua University
- Beijing 100084
- China
| | - BaiXin Liu
- Key Laboratory of Advanced Materials (MOE)
- School of Materials Science and Engineering
- Tsinghua University
- Beijing 100084
- China
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47
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Salvalaglio M, Mazzotti M, Parrinello M. Urea homogeneous nucleation mechanism is solvent dependent. Faraday Discuss 2015; 179:291-307. [DOI: 10.1039/c4fd00235k] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The composition of the mother phase plays a primary role in crystallization processes, affecting both crystal nucleation and growth. In this work, the influence of solvents on urea nucleation has been investigated by means of enhanced sampling molecular dynamics simulations. We find that, depending on the solvent, the nucleation process can either follow a single-step or a two-step mechanism. While in methanol and ethanol a single-step nucleation process is favored, in acetonitrile a two-step process emerges as the most likely nucleation pathway. We also find that solvents have a minor impact on polymorphic transitions in the early stages of urea nucleation. The impact of finite size effects on the free energy surfaces is systematically considered and discussed in relation to the simulation setup.
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Affiliation(s)
- Matteo Salvalaglio
- ETH Zurich
- Institute of Process Engineering
- CH-8092 Zurich
- Switzerland
- Facoltà di Informatica
| | - Marco Mazzotti
- ETH Zurich
- Institute of Process Engineering
- CH-8092 Zurich
- Switzerland
| | - Michele Parrinello
- Facoltà di Informatica
- Istituto di Scienze Computazionali
- Università della Svizzera Italiana
- CH-6900 Lugano
- Switzerland
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48
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Nie C, Tong X, Wu S, Gong S, Peng D. Paraffin confined in carbon nanotubes as nano-encapsulated phase change materials: experimental and molecular dynamics studies. RSC Adv 2015. [DOI: 10.1039/c5ra17152k] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The characteristics of paraffin confined in carbon nanotubes (CNTs) were investigated using experimental and molecular dynamics (MD) methods.
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Affiliation(s)
- Changda Nie
- School of Mechanical Engineering
- Xiangtan University
- Xiangtan 411105
- PR China
| | - Xuan Tong
- School of Mechanical Engineering
- Xiangtan University
- Xiangtan 411105
- PR China
| | - Shuying Wu
- School of Mechanical Engineering
- Xiangtan University
- Xiangtan 411105
- PR China
| | - Shuguang Gong
- School of Mechanical Engineering
- Xiangtan University
- Xiangtan 411105
- PR China
| | - Deqi Peng
- School of Mechanical Engineering
- Xiangtan University
- Xiangtan 411105
- PR China
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49
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Abstract
Despite its ubiquitous character and relevance in many branches of science and engineering, nucleation from solution remains elusive. In this framework, molecular simulations represent a powerful tool to provide insight into nucleation at the molecular scale. In this work, we combine theory and molecular simulations to describe urea nucleation from aqueous solution. Taking advantage of well-tempered metadynamics, we compute the free-energy change associated to the phase transition. We find that such a free-energy profile is characterized by significant finite-size effects that can, however, be accounted for. The description of the nucleation process emerging from our analysis differs from classical nucleation theory. Nucleation of crystal-like clusters is in fact preceded by large concentration fluctuations, indicating a predominant two-step process, whereby embryonic crystal nuclei emerge from dense, disordered urea clusters. Furthermore, in the early stages of nucleation, two different polymorphs are seen to compete.
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50
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Sosso GC, Colombo J, Behler J, Del Gado E, Bernasconi M. Dynamical Heterogeneity in the Supercooled Liquid State of the Phase Change Material GeTe. J Phys Chem B 2014; 118:13621-8. [DOI: 10.1021/jp507361f] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Gabriele C. Sosso
- Department
of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg
1-5 CH-8093 Zurich, Switzerland
- Faculty
of Informatics, Università della Svizzera Italiana, Via
G. Buffi 13, CH-6900 Lugano, Switzerland
| | - Jader Colombo
- Department
of Civil, Environmental and Geomatic Engineering, ETH Zurich, CH-8903 Zurich, Switzerland
| | - Jörg Behler
- Lehrstuhl
für Theoretische Chemie, Ruhr-Universität Bochum, Universitätsstrasse
150, D-44780 Bochum, Germany, and
| | - Emanuela Del Gado
- Department
of Civil, Environmental and Geomatic Engineering, ETH Zurich, CH-8903 Zurich, Switzerland
| | - Marco Bernasconi
- Dipartimento
di Scienza dei Materiali, Università di Milano-Bicocca, Via
R. Cozzi 53, I-20125 Milano, Italy
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