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Additive manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), holds the potential for producing high-quality parts with intricate details. However, optimizing process parameters for arbitrary alloy chemistries to ensure printability remains challenging. This study evaluates machine learning (ML) models to predict a material’s amenability to L-PBF via the printability index, focusing on High Entropy Alloy (HEA) spaces. The printability index of a material is defined as the percentage of the defect-free L-PBF processing window. Our study revealed that CatBoost Regressors and Random Forest Regressors excel in predictive accuracy, consistently yielding predictions with competitive error metrics such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and . In addition, competitive rank-order coefficients such as Spearman and Kendall-tau demonstrated that these models are not overfitting. Feature importance analysis using Shapley Additive Explanations (SHAP) highlighted key material properties influencing printability, such as kinetic viscosity, average Pauling electronegativity, and electric conductivity. While both models performed comparably in predictive accuracy, the Random Forest Regressor demonstrated superior computational efficiency, particularly with large datasets. Robustness tests confirmed its reliability across different test sizes. This research underscores the importance of considering factors like computational efficiency, interpretability, and robustness to noise when selecting ML models for L-PBF material printability prediction. Leveraging Integrated Computational Materials Engineering (ICME) methodologies and ML models can significantly optimize process parameters and material properties, paving the way for innovative solutions in L-PBF. This approach accelerates the assessment of new materials and optimizes existing ones for L-PBF processes, contributing significantly to the field of AM. 

We developed an ML framework to predict Vickers hardness in RHEAs. Feature selection reduced descriptors, enabling Kernel Ridge Regression to achieve the highest accuracy. SHAP identified valence electron concentration as a key predictor, highlighting ML’s potential in alloy design. 

Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of elements.