Pareto-optimization of sustainable high-entropy alloys: balancing hardness and sustainability via machine learning
Source abstract
Abstract
Random Forest and Pareto optimization screen 50 000 virtual HEAs, identifying champions with 80–85% lower embodied energy than conventional superalloys.
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: High Entropy Alloys Studies · Machine Learning in Materials Science · High-Temperature Coating Behaviors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials Science and Engineering, Rajshahi University of Engineering and Technology, Kazla, Rajshahi, 6204, Bangladesh
Metallurgical and Materials Engineering, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
Department of Mechanical Engineering, Rajshahi University of Engineering and Technology, Kazla, Rajshahi, 6204, Bangladesh
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