Energy-free machine learning force field for aluminum

Ivan Kruglov, Oleg Sergeev, Alexey Yanilkin, Artem R. Oganov

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

Original languageEnglish
Article number8512
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Dec 2017

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