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What is the state of art of the use of Machine Learning algorithms in Condensed Matter Physics and Phase transitioning? Is there any promising result that lead us to think this is a good way to pursue?

I already know Perimeter Institute published a famous paper in which they attempted (and succeeded) to apply Machine Learning ideas to Phase transitioning, but I'm wondering if it's only an oasy into a desert or if there are really theoretical groups trying to develop this ideas.

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closed as too broad by Kyle Kanos, John Rennie, sammy gerbil, Yashas, Asher Nov 25 '17 at 7:19

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

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The other big result that I'm familiar with is https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.216401. See https://physics.aps.org/articles/v10/56 for a nice overview.

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