Abstract
The present work formulated a materials design approach, a cluster-formula-embedded machine learning (ML) model, to search for body-centered-cubic (BCC) β-Ti alloys with low Young’s modulus (E) in the Ti–Mo–Nb–Zr–Sn–Ta system. The characteristic parameters, including the Mo equivalence and the cluster-formula approach, are implemented into the ML to ensure the accuracy of prediction, in which the former parameter represents the BCC-β structural stability, and the latter reflects the interactions among elements expressed with a composition formula. Both auxiliary gradient-boosting regression tree and genetic algorithm methods were adopted to deal with the optimization problem in the ML model. This cluster-formula-embedded ML can not only predict alloy property in the forward design, but also design and optimize alloy compositions with desired properties in multicomponent systems efficiently and accurately. By setting different objective functions, several new β-Ti alloys with either the lowest E (E = 48 GPa) or a specific E (E = 55 and 60 GPa) were predicted by ML and then validated by a series of experiments, including the microstructural characterization and mechanical measurements. It could be found that the experimentally obtained E of predicted alloys by ML could reach the desired objective E, which indicates that the cluster-formula-embedded ML model can make the prediction and optimization of composition and property more accurate, effective, and controllable.
Highlights
Β-Ti alloys with a body-centered-cubic (BCC) structure have attracted more attention due to their prominent properties (high strength, low Young’s modulus (E), and good corrosion resistance), showing a great potential for biomedical applications[1,2]
A cluster-formula approach is applied to design alloy compositions in multicomponent systems, which considers the correlations among alloying solute elements and the base element in light of the chemical short-range orders (CSROs) of solid solutions and gives chemical compositions intuitively[15,16,17]
Xue et al carried out the machine learning (ML) surrogate model, together with characteristic parameters, to search for high-entropy alloys with high microhardness in Al–Co–Cr–Cu–Fe–Ni system, in which the predicted results are well consistent with the experiments since these selected characteristic parameters are closely related to the desired property[29]
Summary
Β-Ti alloys with a body-centered-cubic (BCC) structure have attracted more attention due to their prominent properties (high strength, low Young’s modulus (E), and good corrosion resistance), showing a great potential for biomedical applications[1,2]. The introduction of Based on CSROs, we proposed a ‘cluster-plus-glue-atom’ structural any characteristic parameters in the ML process aims mainly at model to describe the local atomic distribution of alloying solute constructing a perfect target-oriented loop of the input In this model, the cluster is the nearest-neighbor compositions) and output (properties) for an accurate and efficient prediction and design.
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