Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows

TL;DR

This paper introduces an autonomous nanoindentation framework using cost-aware Bayesian optimization to efficiently map mechanical properties in combinatorial materials, significantly reducing testing time while maintaining accuracy.

Contribution

It develops a novel adaptive experimental workflow that integrates heteroskedastic Gaussian processes and cost models, improving efficiency in materials characterization.

Findings

Achieves nearly thirty-fold increase in property-mapping efficiency.

Effectively accounts for instrument drift and reconfiguration costs.

Demonstrates generalizability to other high-precision instruments.

Abstract

Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with cost-aware Bayesian optimization to dynamically select indentation locations and hold times, minimizing total testing time while preserving measurement accuracy. A detailed emulator and cost model capture the intrinsic penalties associated with lateral motion, drift stabilization, and reconfiguration-factors often neglected in conventional active-learning approaches. To prevent kernel-length-scale collapse caused by disparate time scales, a hierarchical meta-testing workflow combining local grid and global exploration is introduced. Implementation of the workflow is shown on a experimental Ta-Ti-Hf-Zr thin-film library. The proposed framework achieves nearly a thirty-fold improvement in property-mapping efficiency relative to grid-based indentation, demonstrating that incorporating cost and drift models into probabilistic planning substantially improves performance. This study establishes a generalizable strategy for optimizing experimental workflows in autonomous materials characterization and can be extended to other high-precision, drift-limited instruments.