rexi<p><a href="https://phys.org/news/2023-09-ai-algorithm-microscopic-nematicity-moir.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">phys.org/news/2023-09-ai-algor</span><span class="invisible">ithm-microscopic-nematicity-moir.html</span></a></p><p>"…typically composed of stacks of <a href="https://mastodon.social/tags/graphene" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>graphene</span></a> layers with a relative twist…attracted immense attention from the <a href="https://mastodon.social/tags/condensedmatter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>condensedmatter</span></a> community…due to their high tunability and…make these systems a perfect playground for testing theories from <a href="https://mastodon.social/tags/stronglycorrelatedphenomena" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stronglycorrelatedphenomena</span></a>…but directly obtaining these details from experimental data is often an ill-defined inverse problem…we trained a <a href="https://mastodon.social/tags/convolutionalneuralnetwork" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>convolutionalneuralnetwork</span></a>…to recognize features of <a href="https://mastodon.social/tags/nematicity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nematicity</span></a> from the data…"</p>