Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential.

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at the liquid state is vital for optimizing the manufacture process to produce desired alloys. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. Here, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloys. Ab initio molecular dynamics simulation data on the Al-Ce liquid with a small unit cell (∼200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of the AlCe liquid and crystalline phases of Al-Ce compounds in comparison with the ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of the metallic AlCe liquid, which would provide useful insight into the guiding experimental process to produce desired Al-Ce alloys.