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Title:          Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Subject:        Journal of Physics: Materials, 3 (2020) 02LT02 doi: 10.1088/2515-7639/ab7cbb
Keywords:       machine learning,thermal conductivity,molecular dynamics,density functional theory simulations,Two-dimensional polyaniline C3N monolayer
Author:         Bohayra Mortazavi ,Evgeny V Podryabinkin ,Ivan S Novikov ,Stephan Roche ,Timon Rabczuk ,Xiaoying Zhuang ,Alexander V Shapeev
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      Title: Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
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        Title: 1. Introduction
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        Title: 2. Computational methods
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          Title: 2.1. Ab-initio modelling
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          Title: 2.2. Training of interatomic potentials
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          Title: 2.3. Molecular dynamics modelling
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        Title: 3. Results and discussions
       Item: 
        Title: 4. Conclusion
       Item: 
        Title: Acknowledgments
       Item: 
        Title: References
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   Type: CRC32
  Checksum: ce4fa40e3906ddad1c106af0425c7c62
   Type: MD5
  Checksum: 7de9711a3d4f5e0e611113e5655ff5783f23db84
   Type: SHA-1