Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.

To aid the development of machine learning models for automated spectroscopic data classification, we created a universal synthetic dataset for the validation of their performance. The dataset mimics the characteristic appearance of experimental measurements from techniques such as X-ray diffraction, nuclear magnetic resonance, and Raman spectroscopy among others. We applied eight neural network architectures to classify artificial spectra, evaluating their ability to handle common experimental artifacts. While all models achieved over 98% accuracy on the synthetic dataset, misclassifications occurred when spectra had overlapping peaks or intensities. We found that non-linear activation functions, specifically ReLU in the fully-connected layers, were crucial for distinguishing between these classes, while adding more sophisticated components, such as residual blocks or normalization layers, provided no performance benefit. Based on these findings, we summarize key design principles for neural networks in spectroscopic data classification and publicly share all scripts used in this study.

The self-assembly of DNA-modified silica nanoparticles in microfluidic droplets was investigated using a new fluorosurfactant. Such droplets can be used for cell encapsulation.