Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression

Computational Materials Science

Volumn 50, Issue 2, 2010, Pages 363-372

  Majid, Abdul ^a,c   Khan, Asifullah ^a   Javed, Gibran ^b   Mirza, Anwar M ^d  

^a PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

^b NATIONAL ENGINEERING AND SCIENTIFIC COMMISSION   (Pakistan)

^c Gwangju Institute of Science and Technology (GIST)   (South Korea)

^d NATIONAL UNIVERSITY OF COMPUTER AND EMERGING SCIENCES   (Pakistan)

Author keywords

Artificial Neural Network; Density Functional Theory; Lattice Constant Prediction; Multiple Linear Regression; Perovskites; Support Vector Regression

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; CONVENTIONAL APPROACH; DATA SETS; GENERALIZED REGRESSION NEURAL NETWORKS; IONIC RADIUS; MULTIPLE LINEAR REGRESSIONS; PERFORMANCE ANALYSIS; PHYSIO-CHEMICAL PROPERTIES; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

CRYSTALLINE MATERIALS; DENSITY FUNCTIONAL THEORY; FORECASTING; LATTICE CONSTANTS; LINEAR REGRESSION; TIME VARYING SYSTEMS; VECTORS; WAVE FUNCTIONS;

NEURAL NETWORKS;

EID: 78449258204     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2010.08.028     Document Type: Article

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