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Molecular Simulation Volume 44, 2018 - Issue 11: Machine Learning
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Articles

Representing molecular and materials data for unsupervised machine learning

E. SwannMolecular and Materials Modelling, Data61 CSIRO, Docklands, Victoria, Australia.View further author information
,
B. SunMolecular and Materials Modelling, Data61 CSIRO, Docklands, Victoria, Australia.View further author information
,
D. M. ClelandMolecular and Materials Modelling, Data61 CSIRO, Docklands, Victoria, Australia.Correspondence[email protected]
View further author information
&
A. S. BarnardMolecular and Materials Modelling, Data61 CSIRO, Docklands, Victoria, Australia.View further author information
Pages 905-920 | Received 16 Oct 2017, Accepted 04 Mar 2018, Published online: 02 Apr 2018

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Zhudan Chen, Dazi Li, Haixiao Wan, Minghui Liu & Jun Liu. (2020) Unsupervised machine learning methods for polymer nanocomposites data via molecular dynamics simulation. Molecular Simulation 46:18, pages 1509-1521.
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Lisa Allen, Miren Agote-Arán, Andrew M. Beale, Peixi Cong, Sofia Mediavilla-Madrigal & Stephen W.T. Price. 2023. Comprehensive Inorganic Chemistry III. Comprehensive Inorganic Chemistry III 108 148 .
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Manolo C Per & Deidre M Cleland. (2020) Roadmap on post-DFT methods for nanoscience. Nano Futures 4:3, pages 032004.
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Baichuan Sun & Amanda S Barnard. (2019) Visualising multi-dimensional structure/property relationships with machine learning. Journal of Physics: Materials 2:3, pages 034003.
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Baichuan Sun & Amanda S Barnard. (2018) Texture based image classification for nanoparticle surface characterisation and machine learning. Journal of Physics: Materials 1:1, pages 016001.
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