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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. 

Abstract The in‐plane packing of gold (Au), polystyrene (PS), and silica (SiO₂) spherical nanoparticle (NP) mixtures at a water–oil interface is investigated in situ by UV–vis reflection spectroscopy. All NPs are functionalized with carboxylic acid such that they strongly interact with amine‐functionalized ligands dissolved in an immiscible oil phase at the fluid interface. This interaction markedly increases the binding energy of these nanoparticle surfactants (NPSs). The separation distance between the Au NPSs and Au surface coverage are measured by the maximum plasmonic wavelength (λ_max) and integrated intensities as the assemblies saturate for different concentrations of non‐plasmonic (PS/SiO₂) NPs. As the PS/SiO₂content increases, the time to reach intimate Au NP contact also increases, resulting from their hindered mobility. λ_maxchanges within the first few minutes of adsorption due to weak attractive inter‐NP forces. Additionally, a sharper peak in the reflection spectrum at NP saturation reveals tighter Au NP packing for assemblies with intermediate non‐plasmonic NP content. Grazing incidence small angle X‐ray scattering (GISAXS) and scanning electron microscopy (SEM) measurements confirm a decrease in Au NP domain size for mixtures with larger non‐plasmonic NP content. The results demonstrate a simple means to probe interfacial phase separation behavior using in situ spectroscopy as interfacial structures densify into jammed, phase‐separated NP films.