Content area

Abstract

This study develops a machine learning potential (MLP) based on the Moment Tensor Potential (MTP) method for the TaN-Ce system. This potential is employed to investigate the interfacial structure and wetting behavior between liquid Ce and solid TaN. Molecular dynamics (MDs) simulations reveal that liquid Ce exhibits significant wetting on the TaN surface at high temperatures. The interfacial region undergoes pre-melting and component interdiffusion, forming an amorphous transition layer. Nitrogen atoms display high diffusivity, leading to surface mass loss, while tantalum atoms demonstrate excellent thermal stability and penetration resistance. These findings provide theoretical support for the design of interfacial materials and corrosion control in high-temperature metallurgy.

Details

1009240
Business indexing term
Title
Development of a TaN-Ce Machine Learning Potential and Its Application to Solid–Liquid Interface Simulations
Author
Zhang, Yunhan 1 ; Cai Jianfeng 2 ; Chen, Hongjian 2 ; Lv Xuming 1 ; Bowen, Huang 2   VIAFID ORCID Logo 

 National Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, China; [email protected] (Y.Z.); [email protected] (X.L.), Research Institute of Physics and Chemistry Engineering of Nuclear Industry, Tianjin 300180, China 
 State Key Laboratory of Cemented Carbide, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; [email protected] (J.C.); [email protected] (H.C.) 
Publication title
Metals; Basel
Volume
15
Issue
9
First page
972
Number of pages
16
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20754701
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-08-30
Milestone dates
2025-07-26 (Received); 2025-08-24 (Accepted)
Publication history
 
 
   First posting date
30 Aug 2025
ProQuest document ID
3254604396
Document URL
https://www.proquest.com/scholarly-journals/development-tan-ce-machine-learning-potential/docview/3254604396/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-09-26
Database
ProQuest One Academic