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

Majid, Abdul; Khan, Asifullah; Javed, Gibran; Mirza, Anwar M.

doi:10.1016/j.commatsci.2010.08.028

Cited by 85 publications

(44 citation statements)

References 51 publications

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“…Recently, several works [2][3][4][5][6] were published, which dealt with empirical modeling of the LC for the perovskite crystals. The linear relations between the value of a and several other variables (ionic radii, number of valence electrons, and electronegativity) in several different combinations were proposed and successfully tested.…”

Section: Introductionmentioning

confidence: 99%

Modeling of lattice constant and their relations with ionic radii and electronegativity of constituting ions of A2XY6 cubic crystals (A=K, Cs, Rb, Tl; X=tetravalent cation, Y=F, Cl, Br, I)

Brik

Kityk

2011

Journal of Physics and Chemistry of Solids

109

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Cite this article as: M.G. Brik and I.V. Kityk, Modeling of lattice constant and their relations with ionic radii and electronegativity of constituting ions of A 2 XY 6 cubic crystals (A = K, Cs, Rb, Tl; X = tetravalent cation, Y = F, Cl, Br, I), Journal of Physics and Chemistry of Solids, doi:10.1016Solids, doi:10. /j.jpcs.2011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Introductionmentioning

confidence: 99%

Modeling of lattice constant and their relations with ionic radii and electronegativity of constituting ions of A2XY6 cubic crystals (A=K, Cs, Rb, Tl; X=tetravalent cation, Y=F, Cl, Br, I)

Brik

Kityk

2011

Journal of Physics and Chemistry of Solids

109

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“…However, GRNN and SVR models are performing better as compared to ANN and MLR models. This is because the generalization capability of ANN models is poor as compared to SVR models [15]. Due to the linear nature, the performance of MLR model is poor than GRNN, SVR, and ANN based CI models.…”

Section: Performance Comparison Of CI Modelsmentioning

confidence: 98%

“…Previously, support vector regression (SVR), general regression neural network (GRNN), artificial neural network (ANN), and multiple linear regression (MLR) based CI techniques are used in predicting the lattice structure of perovskites compounds [15,16]. These techniques provide an efficient alternative in modeling material [17], monitoring structures [18], fabrication [19], chip designing [20], and vibration modeling [21].…”

Section: Introductionmentioning

confidence: 99%

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Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques

Majid

Ahsan

Tariq

2015

Applied Soft Computing

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“…It is built on sound mathematical foundation and does not converge to local minima. It has enjoyed a wide range of applications in material sciences [20][21][22][23][24][25], medicine [26,27] and other areas of study [28,29]. Its hybridization proposed in this present work involves combination of two SVR in which one of it is trained and tested using molecular weight and number of carbon to carbon double bound as the descriptors, while the other SVR is developed using the estimated melting points of the first one.…”

Section: Introductionmentioning

confidence: 98%

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

Owolabi

Zakariya

Olatunji

et al. 2016

Neural Comput & Applic

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This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon-carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon-carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVRbased model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points.

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

Cited by 85 publications

References 51 publications

Modeling of lattice constant and their relations with ionic radii and electronegativity of constituting ions of A2XY6 cubic crystals (A=K, Cs, Rb, Tl; X=tetravalent cation, Y=F, Cl, Br, I)

Modeling of lattice constant and their relations with ionic radii and electronegativity of constituting ions of A2XY6 cubic crystals (A=K, Cs, Rb, Tl; X=tetravalent cation, Y=F, Cl, Br, I)

Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

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