2025/11/21 by Vivek Chawla, Stephen Puplampu, Chawla, Vivek +9
Engineering · Materials Science · Physics and Astronomy · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Metal and Thin Film Mechanics #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2511.16930
openalex publication_date 2025/11/21 · openalex created_date 2025/11/25 · openalex updated_date 2026/07/28
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.