Calculation of elastic constants of embedded-atom-model potentials in the NVT ensemble

Physical Review E ◽

10.1103/physreve.103.063307 ◽

2021 ◽

Vol 103 (6) ◽

Author(s):

Menahem Krief ◽

Yinon Ashkenazy

Keyword(s):

Elastic Constants ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02859 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02858 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Pressure–Temperature Phase Diagram of Lithium, Predicted by Embedded Atom Model Potentials

The Journal of Physical Chemistry B ◽

10.1021/acs.jpcb.0c03882 ◽

2020 ◽

Vol 124 (28) ◽

pp. 6015-6023

Author(s):

Jordan Dorrell ◽

Livia B. Pártay

Keyword(s):

Phase Diagram ◽

Temperature Phase ◽

Model Potentials ◽

Embedded Atom ◽

Temperature Phase Diagram

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02862 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02861 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02860 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02880 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02863 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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Improved embedded-atom model potentials of Pb at high pressure: application to investigations of plasticity and phase transition under extreme conditions

Modelling and Simulation in Materials Science and Engineering ◽

10.1088/1361-651x/aaea55 ◽

2018 ◽

Vol 27 (1) ◽

pp. 015001 ◽

Author(s):

Kun Wang ◽

Wenjun Zhu ◽

Meizhen Xiang ◽

Yun Xu ◽

Guomeng Li ◽

...

Keyword(s):

Phase Transition ◽

High Pressure ◽

Extreme Conditions ◽

Model Potentials ◽

Embedded Atom ◽

Pressure Application

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Embedded atom model potentials for metallic nanostructures obtained via machine learning

10.1021/scimeetings.0c02879 ◽

2020 ◽

Author(s):

Andreas Hauser ◽

Ralf Meyer

Keyword(s):

Machine Learning ◽

Metallic Nanostructures ◽

Model Potentials ◽

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