Uncertainty aware design space modeling for sample efficiency in material design of bainitic steels

Abstract

Optimizing sampling efficiency is crucial for solving complex material design challenges, especially with a limited experimental budget. This study focuses on improving sampling efficiency by reducing the search space for carbide-free bainitic steels through the uncertainty-aware modeling of constraints. These constraints include avoiding the formation of undesirable competing phases such as carbides, ferrite, and martensite, as well as accounting for practical limitations on phase transformation durations. Experimental data, obtained through dilatometry and metallography, inform most constraints, except for the presence of carbides. To model these constraints, we use machine learning (ML) models trained on a combination of newly acquired experimental data and experimental data from the literature. Predicting properties in unexplored regions of the design space can lead to inaccuracies. Thus, reliable uncertainty quantification is essential to avoid excluding parts of the design space due to overconfident erroneous predictions. To address this, we employ conformal prediction (CP), a distribution-free framework that provides calibrated post-hoc uncertainty estimates for the different ML models, ensuring reliable extrapolations without prematurely excluding viable design regions. This approach achieves a reduction ranging from 80% to more than 99% depending on the strictness of the employed criteria reduction in the search space, greatly enhancing sampling efficiency without compromising reliability.

Keywords

Uncertainty / machine learning / conformal prediction / bainite