摘要
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-βstructural 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.
基金
It was supported by the National Natural Science Foundation of China[No.91860108 and U1867201]
the National Key Research and Development Plan(2017YFB0702401)
Natural Science Foundation of Liaoning Province of China(Grant No.2019-KF-05-01)
the Fundamental Research Funds for the Central Universities(DUT19LAB01).
