rexi
https://phys.org/news/2023-09-ai-algorithm-microscopic-nematicity-moir.html
"…typically composed of stacks of #graphene layers with a relative twist…attracted immense attention from the #condensedmatter community…due to their high tunability and…make these systems a perfect playground for testing theories from #stronglycorrelatedphenomena…but directly obtaining these details from experimental data is often an ill-defined inverse problem…we trained a #convolutionalneuralnetwork…to recognize features of #nematicity from the data…"
AI algorithm learns microscopic details of nematicity in moiré systems
Identifying and understanding experimental signatures of phases of matter is usually a challenging task due to strong electron interactions in a material and can become even harder due to external influences in samples with the presence of impurities or other sources of deformations. Typically, these interactions between the electrons in a material give rise to fascinating phenomena such as magnetism, superconductivity and electronic nematicity.