fig2

Machine learning-assisted high-entropy alloy discovery: a perspective

Figure 2. ML-assisted multi-objective optimization of mechanical properties in HEAs. (A) ML-driven multi-objective design strategy for high-performance LW-RHEAs; (B) Sequential filter strategy for multi-objective optimization, resulting in the successful design of three alloys with outstanding comprehensive performance. The figures are quoted with permission from Gao et al.[41]; (C) Schematic illustration of the data management workflow for accelerated materials discovery; (D) Example optimization simulation aimed at maximizing room temperature properties derived from thermodynamic calculations. The figures are quoted with permission from Hastings et al.[42].

Journal of Materials Informatics
ISSN 2770-372X (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/