Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models

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Abstract

In several recent years, a significant progress has been made in atomistic simulation of materials, involving the application of machine learning methods to constructing classical interatomic interaction potentials. These potentials are many-body functions with a large number of variable parameters whose values are optimized with the use of energies and forces calculated for various atomic configurations by ab initio methods. In the present paper a machine learning potential is developed on the basis of deep neural networks (DP) for Al–Cu alloys, and the accuracy and performance of this potential is compared with the embedded atom potential. The analysis of the results obtained implies that the DP provides a sufficiently high accuracy of calculation of the structural, thermodynamic, and transport properties of Al–Cu alloys in both solid and liquid states over the entire range of compositions and a wide temperature interval. The accuracy of the embedded atom model (EAM) in calculating the same properties is noticeably lower on the whole. It is demonstrated that the application of the potentials based on neural networks to the simulation on modern graphic processors allows one to reach a computational efficiency on the same order of magnitude as those of the embedded atom calculations, which at least four orders of magnitude higher than the computational efficiency of ab initio calculations. The most important result is that about the possibility of application of DP parameterized with the use of configurations corresponding to melts and perfect crystals to the simulation of structural defects in crystals and interphase surfaces.

About the authors

E. O. Khazieva

Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences; Ural Federal University

Email: cat.hazieva@yandex.ru
620016, Yekaterinburg, Russia; 620002, Yekaterinburg, Russia

N. M. Shchelkachev

Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences; Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences

Email: cat.hazieva@yandex.ru
620016, Yekaterinburg, Russia; 142190, Troitsk, Moscow, Russia

A. O. Tipeev

Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences; Department of Materials Engineering, Federal University of São Carlos

Email: cat.hazieva@yandex.ru
620016, Yekaterinburg, Russia; 13565-905, São Carlos, SP, Brazil

R. E. Ryl'tsev

Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences; Ural Federal University

Author for correspondence.
Email: cat.hazieva@yandex.ru
620016, Yekaterinburg, Russia; 620002, Yekaterinburg, Russia

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