Harnessing the Big Data Paradigm for ICME: Shifting from Materials Selection to Materials Enabled Design

As the size of databases has significantly increased, whether through high throughput computation or through informatics-based modeling, the challenge of selecting the optimal material for specific design requirements has also arisen. Given the multiple, and often conflicting, design requirements, this selection process is not as trivial as sorting the database for a given property value. We suggest that the materials selection process should minimize selector bias, as well as take data uncertainty into account. For this reason, we discuss and apply decision theory for identifying chemical additions to Ni-base alloys. We demonstrate and compare results for both a computational array of chemistries and standard commercial superalloys. We demonstrate how we can use decision theory to select the best chemical additions for enhancing both property and processing, which would not otherwise be easily identifiable. This work is one of the first examples of introducing the mathematical framework of set theory and decision analysis into the domain of the materials selection process.