Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

• Published in: Advanced engineering materials Vol. 26; no. 17
• Main Authors: Lenchuk, Olena, Rohrer, Jochen, Albe, Karsten
• Format: Journal Article
• Language: English
• Published: 01-09-2024
• Subjects: machine‐learning interatomic potentials; molecular dynamics; Mo–Si alloys; refractory alloys
• ISSN: 1438-1656; 1527-2648
• DOI: 10.1002/adem.202302043

A machine‐learning interatomic potential for Mo–Si alloys based on the atomic cluster expansion formalism is presented, its performance is validated, and it is applied for studying interface phenomena. Structural parameters, elastic constants, and melting temperatures of the crystalline body‐centered cubic Mo, diamond Si, and stable Mo–Si alloys (Mo3Si, Mo5Si3, and MoSi2) are calculated and compared to experimental values. Using the trained potential defect, formation energies are calculated and the thermodynamic stability of various MoxSiy alloys is discussed with focus on Mo3Si. Finally, the intermixing between Mo and Si phases is studied by performing interface simulations of Mo|Si. The crystallization behavior of the Mo3Si phase provides additional evidence for the off‐stoichiometric composition of this intermetallic phase.
A novel machine‐learning interatomic potential for Mo–Si alloys is used to investigate the crystallization behavior of the Mo3Si phase and provides evidence for the off‐stoichiometric composition of this intermetallic phase.