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

Lenchuk, Olena ; Rohrer, Jochen ; Albe, Karsten (2024)
Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena.
In: Advanced Engineering Materials, 2024, 26 (17)
doi: 10.26083/tuprints-00028276
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Item Type: Article
Type of entry: Secondary publication
Title: Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena
Language: English
Date: 19 November 2024
Place of Publication: Darmstadt
Year of primary publication: September 2024
Place of primary publication: Weinheim
Publisher: Wiley-VCH
Journal or Publication Title: Advanced Engineering Materials
Volume of the journal: 26
Issue Number: 17
Collation: 12 Seiten
DOI: 10.26083/tuprints-00028276
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

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.

Uncontrolled Keywords: machine‐learning interatomic potentials, molecular dynamics, Mo–Si alloys, refractory alloys
Identification Number: Artikel-ID: 2302043
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-282760
Additional Information:

Special Issue: Materials Compounds from Composite Materials for Applications in Extreme Conditions

Classification DDC: 500 Science and mathematics > 540 Chemistry
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 660 Chemical engineering
Divisions: 11 Department of Materials and Earth Sciences > Material Science > Materials Modelling
Date Deposited: 19 Nov 2024 12:26
Last Modified: 19 Nov 2024 12:29
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/28276
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