Echeverri Restrepo, Sebastián, Tamayo Giraldo, Simón, Thijsse, Barend J. (2014) Using artificial neural networks to predict grain boundary energies. Computational Materials Science, 86. 170-173 doi:10.1016/j.commatsci.2014.01.039

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Reference Type	Journal (article/letter/editorial)
Title	Using artificial neural networks to predict grain boundary energies
Journal	Computational Materials Science
Authors	Echeverri Restrepo, Sebastián		Author
	Tamayo Giraldo, Simón		Author
	Thijsse, Barend J.		Author
Year	2014 (April)	Volume	86
Publisher	Elsevier BV
DOI	doi:10.1016/j.commatsci.2014.01.039Search in ResearchGate
	Generate Citation Formats
Mindat Ref. ID	9508301	Long-form Identifier	mindat:1:5:9508301:2
GUID	0
Full Reference	Echeverri Restrepo, Sebastián, Tamayo Giraldo, Simón, Thijsse, Barend J. (2014) Using artificial neural networks to predict grain boundary energies. Computational Materials Science, 86. 170-173 doi:10.1016/j.commatsci.2014.01.039
Plain Text	Echeverri Restrepo, Sebastián, Tamayo Giraldo, Simón, Thijsse, Barend J. (2014) Using artificial neural networks to predict grain boundary energies. Computational Materials Science, 86. 170-173 doi:10.1016/j.commatsci.2014.01.039
In	(n.d.) Computational Materials Science Vol. 86. Elsevier BV

	Echeverri Restrepo, Sebastián, Sluiter, Marcel H.F., Thijsse, Barend J. (2013) Atomistic relaxation of systems containing plasticity elements. Computational Materials Science, 73. 154-160 doi:10.1016/j.commatsci.2013.03.001
	Echeverri Restrepo, Sebastián, Andric, Predrag (2023) ABC-FIRE: Accelerated Bias-Corrected Fast Inertial Relaxation Engine. Computational Materials Science, 218. 111978 doi:10.1016/j.commatsci.2022.111978
	Rashidi, A.M., Eivani, A.R., Amadeh, A. (2009) Application of artificial neural networks to predict the grain size of nano-crystalline nickel coatings. Computational Materials Science, 45 (2). 499-504 doi:10.1016/j.commatsci.2008.11.016
	Castin, N., Fernández, J.R., Pasianot, R.C. (2014) Predicting vacancy migration energies in lattice-free environments using artificial neural networks. Computational Materials Science, 84. 217-225 doi:10.1016/j.commatsci.2013.12.016
	Timonova, Maria, Thijsse, Barend J. (2010) Thermodynamic properties and phase transitions of silicon using a new MEAM potential. Computational Materials Science, 48 (3). 609-620 doi:10.1016/j.commatsci.2010.02.029
	Gubbels-Elzas, Astrid, Thijsse, Barend J. (2014) Ionic motion during field-assisted oxidation of aluminium studied by molecular dynamics simulations. Computational Materials Science, 90. 196-202 doi:10.1016/j.commatsci.2014.03.062
	Zhang, Yunhe, Thijsse, Barend, Nicola, Lucia (2014) Competition between dislocations and cracks in molecular dynamics simulations of metal nanoimprinting. Computational Materials Science, 94. 95-105 doi:10.1016/j.commatsci.2014.02.016
	*Lazić, Ivan, Thijsse, Barend J. (2012) An improved molecular dynamics potential for the Al–O system. Computational Materials Science, 53 (1). 483-492 doi:10.1016/j.commatsci.2011.08.021*
	*Elzas, Astrid, Thijsse, Barend (2017) Cohesive law describing crack growth at iron/precipitate interfaces. Computational Materials Science, 134. 214-224 doi:10.1016/j.commatsci.2017.01.042*
	Scopece, Daniele, Thijsse, Barend J. (2015) Comment on: “An improved molecular dynamics potential for the Al–O system” Computational Materials Science 53, 483 (2012). Computational Materials Science, 104. 143-146 doi:10.1016/j.commatsci.2015.03.050
	Timonova, Maria, Thijsse, Barend J. (2011) Molecular Dynamics simulations of the formation and crystallization of amorphous Si. Computational Materials Science, 50 (8). 2380-2390 doi:10.1016/j.commatsci.2011.03.016

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Echeverri Restrepo, Sebastián, Tamayo Giraldo, Simón, Thijsse, Barend J. (2014) Using artificial neural networks to predict grain boundary energies. Computational Materials Science, 86. 170-173 doi:10.1016/j.commatsci.2014.01.039

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