Bayesian Optimization of 7-component (AlVCrFeCoNiMo) single crystal alloy’s compositional space to optimize elasto-plastic properties from Molecular Dynamics Simulations DOI Creative Commons
David Kurunczi-Papp, Lasse Laurson

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 29, 2024

Abstract Exploring the vast compositional space of high-entropy alloys promises materials with superior mechanical properties much needed in industrial applications. We demonstrate on 7-component alloy AlVCrFeCoNiMo system randomly ordered atoms that this exploration can be accelerated by combining molecular dynamics simulations Bayesian optimization. Our algorithm is tested maximizing shear modulus, resulting pure Mo, an unsurprising result based Mo’s large density. Maximizing yield stress results Co-, Cr- and Ni-based optimal composition varying depending presence defects within crystal. Finally, we optimize plastic behaviour aiming for high stresses while minimizing deformation fluctuations, find a predominantly NiMo alloy’s lattice distortions ensure smooth response. The suggest 2- to 4-component optimized may those equiatomic without short-range order.

Language: Английский

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces DOI
Sk Md Ahnaf Akif Alvi, Jan Janßen, Danial Khatamsaz

et al.

Acta Materialia, Journal Year: 2025, Volume and Issue: unknown, P. 120908 - 120908

Published: March 1, 2025

Language: Английский

Citations

2
Thermogelation of methylcellulose: A rheological approach with Gaussian Process Regression DOI Creative Commons

Marie Sourroubille,

Isaac Yair Miranda‐Valdez, Tero Mäkinen

et al.

Colloids and Surfaces A Physicochemical and Engineering Aspects, Journal Year: 2025, Volume and Issue: 709, P. 136057 - 136057

Published: Jan. 7, 2025

Language: Английский

Citations

0
Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties DOI Creative Commons
Tero Mäkinen,

Anshul D. S. Parmar,

Silvia Bonfanti

et al.

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 7, 2025

Language: Английский

Citations

0
Data-driven body-centered cubic phase prediction in cobalt-free high-entropy alloys DOI

Xuliang Luo,

Yulin Li, Tero Mäkinen

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112464 - 112464

Published: April 1, 2025

Language: Английский

Citations

0
Bayesian Optimization of 7-component (AlVCrFeCoNiMo) single crystal alloy’s compositional space to optimize elasto-plastic properties from Molecular Dynamics Simulations DOI Creative Commons
David Kurunczi-Papp, Lasse Laurson

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 29, 2024

Abstract Exploring the vast compositional space of high-entropy alloys promises materials with superior mechanical properties much needed in industrial applications. We demonstrate on 7-component alloy AlVCrFeCoNiMo system randomly ordered atoms that this exploration can be accelerated by combining molecular dynamics simulations Bayesian optimization. Our algorithm is tested maximizing shear modulus, resulting pure Mo, an unsurprising result based Mo’s large density. Maximizing yield stress results Co-, Cr- and Ni-based optimal composition varying depending presence defects within crystal. Finally, we optimize plastic behaviour aiming for high stresses while minimizing deformation fluctuations, find a predominantly NiMo alloy’s lattice distortions ensure smooth response. The suggest 2- to 4-component optimized may those equiatomic without short-range order.

Language: Английский

Citations

0