|About this Abstract
||1st World Congress on High Entropy Alloys (HEA 2019)
||High Entropy Alloys 2019
||High Entropy Alloy Design Using Multiscale Modeling and Machine Learning Based High-throughput Computing
||Anssi Laukkanen, Tomi Suhonen, Matti Lindroos, Tatu Pinomaa, Tom Andersson
|On-Site Speaker (Planned)
High entropy alloys (HEAs) promise a vast design base even with respect to basic alloy chemistry. This is extended further considering the often multiphase microstructure with its added characteristics and additional strengthening mechanisms. To explore and exploit this design space, respective design and discovery methods need to be introduced. The traditional approaches for HEA design are simple but crude, they are not expected to be able to discover the HEAs which in many applications could become the next generation materials for extreme conditions. To that effect, we introduce a workflow which merges both physics-based materials modeling in the atomistic and microstructural regimes and machine learning for optimization and discovery. Case studies are presented sampling the alloy space and optimizing the findings with respect to different property and performance metrics. Links to experimental work synthesizing the materials are included.