ICME and machine learning approach for the accelerated design and development of advanced materials

The design and development of new advanced materials are required for the efficient utilization of resources and energy. Recently the development of high entropy alloys (HEAs) getting wider attention due to their remarkable properties. Identifying the optimum composition for HEAs is challenging due to the availability of large compositional space. Various methods have been proposed for the accelerated screening of HEAs with the required phases and properties. The simulation-guided HEA development approach is promising for the accelerated identification and processing of HEAs. Integrated Computational Materials Engineering (ICME) is getting wider attention for HEA design, and recently the application of artificial intelligence has shown a remarkable improvement in the accelerated design of HEAs. The current talk includes few case studies which show the accelerated design and development of HEAs using a combined ICME and machine learning approach. The phase and mechanical properties of new HEAs are predicted by the integrated framework. (author)