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 liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with 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 Al90Ce10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al90Ce10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.
- Research Organization:
- Ames Laboratory (AMES), Ames, IA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Natural Science Foundation of China (NSFC); USDOE
- Grant/Contract Number:
- AC02-07CH11358; 11304279
- OSTI ID:
- 1833053
- Alternate ID(s):
- OSTI ID: 1830424
- Report Number(s):
- IS-J-10,637; 11304279; TRN: US2216877
- Journal Information:
- Journal of Chemical Physics, Vol. 155, Issue 19; ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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