most citedMachine-Learning-Based Intelligent Framework for Discovering Refractory High-Entropy Alloys with Improved High-Temperature Yield Strength

5 citations · 7 across the 2 of their papers we have counts for

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cond-mat.mtrl-sci2023★ 2 cited

Yield Strength-Plasticity Trade-Off and Uncertainty Quantification for Machine-learning-based Design of Refractory High-Entropy Alloys

Stephen A. Giles, Hugh Shortt, Peter K. Liaw +1

Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in…

cond-mat.mtrl-sci2021★ 5 cited

Machine-Learning-Based Intelligent Framework for Discovering Refractory High-Entropy Alloys with Improved High-Temperature Yield Strength

Stephen A. Giles, Debasis Sengupta, Scott R. Broderick +1

Refractory high-entropy alloys (RHEAs) are a promising class of alloys that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas…