Affiliation:
1. MOE Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, School of Physical Science and Technology, Northwestern Polytechnical University , Xi'an 710072, China
Abstract
The establishment of reliable materials genome databases involving the thermophysical properties of liquid metals and alloys promotes the progress of materials research and development, whereas acquiring these properties imposes great challenges on experimental investigation. Here, we proposed a deep learning method and achieved a deep neural network (DNN) interatomic potential for the entire Ti–Ni–Cr–Al system from pure metals to quaternary alloys. This DNN potential exhibited sufficient temperature and compositional transformability which extended beyond the training and provided the prediction of the liquid structure and thermophysical properties for metallic materials with both density functional theory accuracy and classic molecular dynamics efficiency. The predicted results agreed well with the reported experimental data. This work opens a feasible way to address the challenges of rapidly and accurately acquiring thermophysical properties data for liquid pure metals and multicomponent alloys, covering a broad temperature range from superheated to undercooled state.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Science Fund for Scientific and Technological Innovation Team of Shaanxi Province
Subject
Physics and Astronomy (miscellaneous)
Cited by
2 articles.
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