RT info:eu-repo/semantics/article
T1 A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties
A1 Álvarez Zapatero, Pablo
A1 Vega Hierro, Andrés
A1 Aguado Rodríguez, Andrés
K1 Atomistic simulations
K1 Simulaciones atomísticas
K1 Artificial neural networks
K1 Redes neuronales artificiales
K1 Density functional theory
K1 Teoría del funcional de densidad
K1 Magnesium alloys
K1 Aleaciones de magnesio
AB The accurate description of the potential energy landscape of moderate-sized nanoparticles is a formidable task, but of paramount importance if one aims to characterize, in a realistic way, their physical and chemical properties. We present here a Neural Network potential able to predict structures of pure and mixed nanoparticles with an error in energy and forces of the order of chemical accuracy as compared with the values provided by the theoretical method used in the training process, in our case the density functional theory. The neural network is integrated into a basin-hopping algorithm which dynamically feeds the training process. The main ingredients of the neural network algorithm as well as the protocol used for its implementation and training are detailed, with particular emphasis on those aspects that make it so efficient and transferable. As a first test, we have applied it to the determination of the global minimum structures of ZnMg nanoalloys with up to 52 atoms and stoichiometries corresponding to MgZn and MgZn, of special interest in the context of anticorrosive coatings. We present and discuss the structural properties, chemical order, stability and pertinent electronic indicators, and we extract some conclusions on fundamental aspects that may be at the roots of the good performance of ZnMg nanoalloys as protective coatings. Finally, we comment on the step forward that the presented machine learning approach constitutes, both in the fact that it allows to accurately explore the potential energy surface of systems that other methodologies can not, and that it opens new prospects for a variety of problems in Materials Science.
PB Elsevier
SN 1359-6454
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/49024
UL https://uvadoc.uva.es/handle/10324/49024
LA eng
NO Acta Materialia, 2021, vol. 220, 117341
NO Producción Científica
DS UVaDOC
RD 29-may-2024