|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||A feasibility study of machine learning-assisted alloy design
||Yasaman Jamalipour Soofi, Jinling Liu, Yijia Gu
|On-Site Speaker (Planned)
Machine learning (ML) often requires large datasets for reliable predictions, which may not be feasible for most commercial alloy systems. Also, the alloy development requires a full set of balanced properties, many of which have not been thoroughly investigated by ML. In this study, we focused on the practicality and reliability of ML in estimating alloy properties with a realistic small dataset of commercial wrought aluminum alloys. We have compiled a small but comprehensive dataset that contains 236 entries with 6 mechanical properties and 9 technological properties. We systematically evaluated the predictive performance of several popular ML models with a focus on the bias-variance trade-off, a central problem in training supervised ML models. Moreover, we looked into the prospect of improving ML models by engineering the feature space. This study demonstrated that alloy design may be aided by using machine learning and data mining techniques on realistic small datasets.
||Planned: Other (describe below)