|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||CRADLE a Data Infrastructure for Printable Corrosion-Resistant Alloys
||Xiaoli Yan, Pikee Priya, Phalgun Nelaturu, Dan Thoma, Santanu Chaudhuri
|On-Site Speaker (Planned)
Corrosion-Resistant Alloy Design and Lifetime Evaluations (CRADLE) platform is designed to combine first-principles data, machine learning, and data visualization for improving corrosion resistance. The platform consists of a database server, a data processing server, a machine learning (ML) server, a multi-objective optimization code, and a web-based interactive GUI. The MongoDB database can use in-house first-principles calculation results and open public access databases. The closed-loop corrosion prediction and alloy design using surface energy, work functions, and texture in a machine-learning model can be improved by users using their own data. The CRADLE framework can fuse data sets, use ML to predict higher corrosion-resistant phases, and launch first-principles calculations as needed. We will demonstrate the case of printable high entropy alloys in search of more corrosion-resistant and single-phase high-entropy alloys in the Fe-rich and low-Ni phases. The CRADLE is open access with web-based with local Docker installation.
||Planned: Other (describe below)