Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and design of materials. However, the soft-matter field has lagged far behind in embracing this approach for materials design. The main difficulty stems from the importance of entropy, the ubiquity of...
Segmentation and analysis of structures in 3d biological samples may be an ambiguous operation, due to the difficulties in the data visualization. Machine learning may help for this kind of task, but they may remain opaque regarding the scientific reasons leading to a particular result. Thanks to recent advancement in the field of explainable machine learning, human interpretable explanations...
