Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale.To study the powder samples that are widely used in materials development,scanning electron microscopes(SEMs)are increasingly used at the laboratory scale to generate large datasets with hundreds of images.Parsing these images to identify distinct particles and determine their morphology requires careful analysis,and automating this process remains challenging.In this work,we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images.We address several challenges inherent to measurements,such as image blur and particle agglomeration.Moreover,our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle.Recognizing that disparate length scales are often present in large datasets,we use this framework to create two models that are separately trained to handle images obtained at low or high magnification.By testing these models on a variety of inorganic samples,our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts,revealing comparable accuracy while requiring a fraction of the time.These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction(XRD)patterns,we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions(PDFs).A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation,which accounts for experimental artifacts such as lattice strain and crystallographic texture.A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns.At inference,these networks classify unknown samples by aggregating their predictions in a confidenceweighted sum.We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation.Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples,networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important.These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

Validating neural networks for spectroscopic classification on a universal synthetic dataset

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.