High-Accuracy Machine-Learned Interatomic Potentials for the Phase Change Material Ge<sub>3</sub>Sb<sub>6</sub>Te<sub>5</sub>-Reference-Cited by-同舟云学术

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High-Accuracy Machine-Learned Interatomic Potentials for the Phase Change Material Ge₃Sb₆Te₅

Published:2023-08-14 Issue:17 Volume:35 Page:6651-6658
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Yu Wei¹^ORCID,Zhang Zhaofu²^ORCID,Wan Xuhao¹,Su Jinhao¹,Gui Qingzhong¹,Guo Hailing¹,Zhong Hong-xia³,Robertson John¹⁴,Guo Yuzheng¹^ORCID

Affiliation:

1. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

2. The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China

3. School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China

4. Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, U.K.

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.3c00524

Reference68 articles.

1. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing

2. Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing

3. Accurate deep neural network inference using computational phase-change memory

4. Chemical phase segregation during the crystallization of Ge-rich GeSbTe alloys

5. Structural phase transitions on the nanoscale: The crucial pattern in the phase-change materialsGe2Sb2Te5and GeTe

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review;Journal of the American Ceramic Society;2024-06-09

2. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations;iScience;2024-05

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