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Abstract Over the past three decades, nanoindentation has continuously evolved and transformed the field of materials mechanical testing. Once highlighted by the groundbreaking Oliver–Pharr method, the utility of nanoindentation has transcended far beyond modulus and hardness measurements. Today, with increasing challenges in developing advanced energy generation and electronics technologies, we face a growing demand for accelerated materials discovery and efficient assessment of mechanical properties that are coupled with modern machine learning-assisted approaches, most of which require robust experimental validation and verification. To this end, nanoindentation finds its unique strength, owing to its small-volume requirement, of fast-probing and providing a mechanistic understanding of various materials. As such, this technique meets the demand for rapid materials assessment, including semiconductors, ceramics, and thin films, which are integral to next-generation energy-efficient and high-power electronic devices. Here, we highlight modern nanoindentation strategies using novel experimental protocols outlined by the use of nanoindentation for characterizing functional structures, dislocation engineering, high-speed nanoindentation mapping, and accelerating materials discovery via thin-film libraries. We demonstrate that nanoindentation can be a powerful tool for probing the fundamental mechanisms of elasticity, plasticity, and fracture over a wide range of microstructures, offering versatile opportunities for the development and transition of functional materials. Graphical abstract 

Powder X‐ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high‐throughput discovery in self‐driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low‐quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X‐ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, for example, through transfer learning methods. We collected 92,552 diffractograms, 2179 of them labeled, from a wide spectrum of material classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self‐driving materials labs.