Literature DB >> 31068465

Machine learning provides realistic model of complex phase transition.

Sandro Scandolo1.   

Abstract

Year:  2019        PMID： 31068465      PMCID： PMC6534975          DOI： 10.1073/pnas.1905457116

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN： 0027-8424            Impact factor:   11.205


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  4 in total

1.  Ab initio molecular dynamics study of first-order phase transitions: melting of silicon.

Authors: 
Journal:  Phys Rev Lett       Date:  1995-03-06       Impact factor: 9.161

2.  Generalized neural-network representation of high-dimensional potential-energy surfaces.

Authors:  Jörg Behler; Michele Parrinello
Journal:  Phys Rev Lett       Date:  2007-04-02       Impact factor: 9.161

3.  On the chain-melted phase of matter.

Authors:  Victor Naden Robinson; Hongxiang Zong; Graeme J Ackland; Gavin Woolman; Andreas Hermann
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-11       Impact factor: 11.205

4.  Structural prediction of host-guest structure in lithium at high pressure.

Authors:  Prutthipong Tsuppayakorn-Aek; Wei Luo; Teeraphat Watcharatharapong; Rajeev Ahuja; Thiti Bovornratanaraks
Journal:  Sci Rep       Date:  2018-03-27       Impact factor: 4.379
  4 in total
  1 in total

1.  Machine learning methods trained on simple models can predict critical transitions in complex natural systems.

Authors:  Smita Deb; Sahil Sidheekh; Christopher F Clements; Narayanan C Krishnan; Partha S Dutta
Journal:  R Soc Open Sci       Date:  2022-02-16       Impact factor: 2.963
  1 in total

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