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Aug 2026
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This study presents an integrated, interpretable machine learning framework applied to a dataset of 2,672 multi-component alloy systems, using elemental composition as the sole input, providing a scalable and interpretable foundation for accelerated alloy screening, property prediction, and data-driven materials discov...
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· Journal of engineering and a... · 0 citations
Open access
Aug 2026
Flowability prediction and artificial intelligence analysis of SCC based on machine learning
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, and the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance.
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Open access
Jul 2026
Validation of a Machine Learning–Assisted LIBS Model for Quantitative Steel Analysis
Laser-induced breakdown spectroscopy (LIBS) shows great promise for the rapid chemical characterization of materials. However, quantitative analysis of elements remains challenging due to strong matrix effects and the predominance of emission lines. In the present study, a machine learning–assisted LIBS model (LIBS-ML...
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Open access
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Phase-stability-guided explainable machine learning for mechanical-property prediction and experimentally supported candidate screening of high-entropy alloys
High-entropy alloys (HEAs) provide a broad compositional space for developing structural materials with balanced phase stability and mechanical performance. However, reliable mechanical-property prediction remains challenging because alloy chemistry, phase constitution, processing state, and model uncertainty are s...
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Open access
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Interpretable Deep Learning–Machine Learning Models for Accelerated Discovery of Elastic Properties in Refractory High‐Entropy Alloys
Refractory high‐entropy alloys (RHEAs) offer mechanical performance at extreme temperatures, but their optimization is hindered by the vast compositional space and high cost of first‐principles calculations. This study establishes an interpretable machine‐learning benchmark for predicting nine elastic and mechanical pr...
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Open access
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Three tree-based machine learning models were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset, demonstrating the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datase...
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