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Feature Selection for CCA Strength Models
A tool to evaluate dataset of hardness and strength values of complex-concentrated-alloys. Feature selection, optimization, and explanation methods are included.
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Abstract
The inherent high dimensionality of complex concentrated alloy (CCA) design prohibits full exploration of the material space via experimental means. Therefore, large efforts to model properties and phenomena of the design space coupled with validation is critical for efficient procedure. Since
available datasets are limited, we often turn to machine learning models with carefully engineered features. With increased feature count is the reward of a more complex and accurate model. However, this is often at the cost of interpretability of individual features. In this study we
develop random forest regression models with quantified uncertainties to predict the yield strength of CCAs, followed by an analysis of our selected features using game theory approximations. We use the methods of Shapely coefficients to score and evaluate the impact of our features, and offer
explanations for individual feature impact on model predictions.
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References
Initial Database:
https://citrination.com/datasets/190954/show_search?searchMatchOption=fuzzyMatch
S. Gorsse, M.H. Nguyen, O.N. Senkov, and D.B. Miracle. Database on the mechanical properties of high entropy alloys and complex concentrated alloys. Data in Brief, 21:26, 2678, 2018
Francesco Maresca and William A Curtin. Mechanistic origin of high strength in refractory bcc high entropy alloys up to 1900k. Acta Materialia, 182:235–249, 2020
Daniel B Miracle and Oleg N Senkov. A critical review of high entropy alloys and related concepts. Acta Materialia, 122:448–511, 2017.
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. From local explanations to global understanding with explainable ai for trees. Nature machine intelligence, 2(1):56–67, 2020
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