TL;DR
This work introduces a machine learning (ML) driven iterative framework for the accelerated design of low-liquidus-temperature Ti-based amorphous alloys in multicomponent alloy systems and identifies the optimal amorphous composition Ti60Cu11Ni19Zr10, exhibiting a low liquidus temperature of 1121 K.
Stainless steel is widely used across industries due to its superior corrosion resistance, which is continuously optimized to enhance reliability and performance. This study employs machine learning to predict the passivation current density (ipass) of stainless steels in chloride-containing environments under varying...
This proposed interpretable machine learning strategy provides a data-driven route for designing as-cast Al–Si–Mg alloys with a balance between strength and ductility.
Identifying alloy compositions suitable for metallic glass formation from the vast and multi-dimensional space of elemental combinations remains a significant challenge. In the present study, we develop—using literature data—a Bayesian machine learning model based on Gaussian process classification for predicting the g...
A comprehensive database was constructed, comprising over twenty thousand hardness values from steels with various compositions and nitriding parameters, and two predictive models were developed incorporating physically based parameters such as nitrogen diffusion distance and nitriding layer thickness.
This study presents a high-precision predictive model for continuous cooling transformation (CCT) behavior in steel by integrating physical metallurgy (PM) principles with machine learning (ML) methods. To enhance industrial applicability, thermal deformation parameters are incorporated alongside chemical composition a...
Composition-based machine learning for Al-Mg-Si alloys lacks generalization. A physics-informed strategy incorporating physicochemical descriptors was proposed, improving the coefficient of determination (R2) by 30% over composition-only models. Shapley additive explanations (SHAP) identified electronegativity differen...
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