Literature DB >> 18204218

Predicting regularities in lattice constants of GdFeO3-type perovskites.

Asifullah Khan1, Syed Gibran Javed.   

Abstract

A novel idea of employing genetic programming to obtain mathematical expressions representing the dependency of lattice constants (LC) on their atomic parameters is presented in this paper. The results obtained from simulations reveal that only two atomic parameters are sufficient for LC prediction of GdFeO(3)-type perovskites. In addition, an advantage of this approach is that there is no need to save any trained model as in the case of other existing machine-learning based approaches.

Year:  2008        PMID: 18204218     DOI: 10.1107/S0108768107057527

Source DB:  PubMed          Journal:  Acta Crystallogr B        ISSN: 0108-7681


  2 in total

1.  Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles.

Authors:  Xiaowei Zhao; Jiakui Li; Yanxin Huang; Zhiqiang Ma; Minghao Yin
Journal:  Int J Mol Sci       Date:  2012-03-19       Impact factor: 6.208

2.  Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements.

Authors:  Anna Machrowska; Jakub Szabelski; Robert Karpiński; Przemysław Krakowski; Józef Jonak; Kamil Jonak
Journal:  Materials (Basel)       Date:  2020-11-28       Impact factor: 3.623

  2 in total

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