Unveiling phase transitions with machine learning

Understand

The classification of phase transitions is a central and challenging task in condensed matter physics.

• Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives.
• Here, we propose an alternative framework to identify quantum phase transitions, employing both unsupervised and supervised machine learning techniques.
• Using the axial next-nearest neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect three phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating phase) as well as two distinct regions within the paramagnetic phase.