Pareto-optimization of sustainable high-entropy alloys: balancing hardness and sustainability via machine learning

Md Mashiur Rahman; Md. Moinul Islam; S.M.Nasim Rokon; Md. Mostafa Kamal

doi:10.1039/d6ta02605b

January 1, 2026Journal of Materials Chemistry A

Pareto-optimization of sustainable high-entropy alloys: balancing hardness and sustainability via machine learning

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Key Points

The aim is to identify sustainable high-entropy alloys (HEAs) that balance hardness and reduce embodied energy.
Utilized Random Forest algorithms for screening 50,000 virtual HEAs.
Implemented Pareto optimization to select optimal alloys for sustainability and hardness.
Identified HEAs with 80-85% lower embodied energy compared to conventional superalloys.

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Implication

Study optimizes hardness and sustainability in high-entropy alloys, suggesting lower energy usage.

Abstract

Random Forest and Pareto optimization screen 50 000 virtual HEAs, identifying champions with 80–85% lower embodied energy than conventional superalloys.

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Cite This Study

Rahman et al. (2026) studied this question.

synapsesocial.com/papers/6a1295e248a0ea16656723af https://doi.org/10.1039/d6ta02605b

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Pareto-optimization of sustainable high-entropy alloys: balancing hardness and sustainability via machine learning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Pareto-optimization of sustainable high-entropy alloys: balancing hardness and sustainability via machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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