(1. Materials Genome Institute, Shanghai University, Shanghai 200444, China) (2. International Centre for Quantum and Molecular Structures, Department of Physics, Shanghai University, Shanghai 200444, China)
The composition design of multi-component alloy for the target performance is extremely challenging due to the enormous potential composition. The traditional trial-anderror experiments can only explore limited alloy compositions because of its low efficiency. In this work, the composition design of non-equimolar hard high-entropy alloy CoxCryTizMouWv was accelerated via combining the high-throughput experiment with machine learning. Firstly, 138 as-cast high-entropy alloys were prepared by a home-developed all-process high-throughput alloy synthesis system. Then, the machine learning models were built based on the measured Vickers hardness (HV) by using random forest (RF) and supporting vector machine methods. And, they made the prediction of HV values for 3876 potential alloys in the fivecomponent alloy system. The HV values predicted by RF machine learning models have the averaged errors of 2.87%, 3.30% and 6.70%, respectively in high (HV>800 MPa), medium (600