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Harnessing machine learning potentials to understand the functional properties of phase-change materials
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Sosso, Gabriele C. and Bernasconi, M. (2019) Harnessing machine learning potentials to understand the functional properties of phase-change materials. MRS Bulletin, 44 (09). pp. 705-709. doi:10.1557/mrs.2019.202 ISSN 0883-7694.
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WRAP-harnessing-machine-learning-potentials-functional-phase-change-materials-Sossos-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1934Kb) | Preview |
Official URL: http://dx.doi.org/10.1557/mrs.2019.202
Abstract
The exploitation of phase-change materials (PCMs) in diverse technological applications can be greatly aided by a better understanding of the microscopic origins of their functional properties. Over the last decade, simulations based on electronic-structure calculations within density functional theory (DFT) have provided useful insights into the properties of PCMs. However, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for the performance of devices. One way to overcome the limitations of DFT methods is to use machine learning (ML) techniques to build interatomic potentials for fast molecular dynamics simulations that still retain a quasi-ab initio accuracy. Here, we review the insights gained on the functional properties of the prototypical PCM GeTe by harnessing such interatomic potentials. Applications and future challenges of the ML techniques in the study of PCMs are also outlined.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing |
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Library of Congress Subject Headings (LCSH): | Molecular dynamics, Machine learning, Materials at low temperatures, Materials at high temperatures, Nonvolatile random-access memory, Materials -- Thermal properties | ||||||||
Journal or Publication Title: | MRS Bulletin | ||||||||
Publisher: | Cambridge University Press | ||||||||
ISSN: | 0883-7694 | ||||||||
Official Date: | 5 September 2019 | ||||||||
Dates: |
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Volume: | 44 | ||||||||
Number: | 09 | ||||||||
Page Range: | pp. 705-709 | ||||||||
DOI: | 10.1557/mrs.2019.202 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Copyright Holders: | © Materials Research Society 2019 | ||||||||
Date of first compliant deposit: | 1 November 2019 | ||||||||
Date of first compliant Open Access: | 5 March 2020 |
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