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Found 4 papers, 2 papers with code
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been used to learn the Boltzmann distribution directly, without samples.
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.
Graph neural networks for materials science and chemistry
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e. g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials.
Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis
Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively.
Materials Science Instrumentation and Detectors
