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

TL;DR

This paper presents a Bayesian multi-objective framework for discovering NiTi shape memory alloys with targeted properties, efficiently guiding experiments and microstructure design using Gaussian process models and optimal sampling strategies.

Contribution

It introduces a novel Bayesian optimization scheme with a closed-loop experimental design for multi-objective materials discovery, applied to NiTi alloys with micromechanical modeling.

Findings

Efficient identification of alloy compositions with desired properties.

Reduced number of computational experiments needed.

Successful application to NiTi shape memory alloys.

Abstract

In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed. The capabilities of the framework are demonstrated on an example case related to the discovery of precipitation strengthened NiTi shape memory alloys with up to three desired properties. In the presented case the framework is used to carry out an efficient search of the shape memory alloys with desired properties while minimizing the required number of computational experiments. The developed scheme features a Bayesian optimal experimental design process that operates in a closed loop. A Gaussian process regression model is utilized in the framework to emulate the response and uncertainty of the physical/computational data while the sequential exploration of the materials design space is carried out by using an optimal policy based on the expected hyper-volume improvement acquisition function. This scalar metric provides a measure of the utility of querying the materials design space at different locations, irrespective of the number of objectives in the performed task. The framework is deployed for the determination of the composition and microstructure of precipitation-strengthened NiTi shape memory alloys with desired properties, while the materials response as a function of microstructure is determined through a thermodynamically-consistent micromechanical model.