期刊
ACTA MATERIALIA
卷 240, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2022.118314
关键词
Atomic ordering; Kinetic Monte Carlo; Artificial neural networks; Atomistic modeling; Atomic structure
资金
- Element Strategy Initiative for Structural Materials (ESISM) of MEXT [JP- MXP01121010 0 0]
- JSPS KAKENHI [JP17H01238, JP17K18827, JP18H05453]
Recent experimental observations suggest the formation of local chemical order in medium-entropy alloys and high-entropy alloys (MEAs/HEAs). This study utilizes simulations to reveal the atomic structure and formation kinetics of the chemical domain structure in FCC CrCoNi MEA, providing key information for controlling chemical order through thermal processing.
The formation of local chemical order in medium-entropy alloys and high-entropy alloys (MEAs/HEAs) has been strongly suggested in recent experimental observations. Since chemical order can lead to changes in mechanical and functional properties, tailoring of chemical order is a promising approach for further improving those properties of MEAs and HEAs. However, details remain unclear regarding the atomic structure of the chemical order and the formation kinetics. Here, employing a large-scale Monte Carlo/molecular dynamics hybrid annealing simulation with a neural network potential, we find a chemical-domain structure (CDS) after annealing below 800 K in FCC CrCoNi MEA. In addition, the formation kinetics, such as the formation time and process and time-temperature-chemical-order diagrams of the CDS, were successfully obtained using a kinetic Monte Carlo simulation with artificial neural network acceleration. The findings provide key information for controlling chemical order via thermal processing.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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