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In this study, a framework for the multi-objective materials discovery basedon Bayesian approaches is developed. The capabilities of the framework aredemonstrated on an example case related to the discovery of precipitationstrengthened NiTi shape memory alloys with up to three desired properties. Inthe presented case the framework is used to carry out an efficient search ofthe shape memory alloys with desired properties while minimizing the requirednumber of computational experiments. The developed scheme features a Bayesianoptimal experimental design process that operates in a closed loop. A Gaussianprocess regression model is utilized in the framework to emulate the responseand uncertainty of the physical/computational data while the sequentialexploration of the materials design space is carried out by using an optimalpolicy based on the expected hyper-volume improvement acquisition function.This scalar metric provides a measure of the utility of querying the materialsdesign space at different locations, irrespective of the number of objectivesin the performed task. The framework is deployed for the determination of thecomposition and microstructure of precipitation-strengthened NiTi shape memoryalloys with desired properties, while the materials response as a function ofmicrostructure is determined through a thermodynamically-consistentmicromechanical model.

Multi-Objective Bayesian Materials Discovery: Application on the  Discovery of Precipitation Strengthened NiTi Shape Memory Alloys through  Micromechanical Modeling