Unveiling phase transitions with machine learning
This provides a computational-friendly method for physicists studying phase transitions in many-body systems, though it is incremental as it builds on existing ML approaches.
The authors tackled the classification of quantum phase transitions in condensed matter physics by developing a machine learning framework using unsupervised and supervised techniques, achieving detection of multiple phases and transfer learning with few low-dimensional inputs (up to 12 lattice sites).
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. Employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbour interactions can learn to identify a new type of phase occurring when next-nearest-neighbour interactions are introduced. All our results rely on few and low dimensional input data (up to twelve lattice sites), thus providing a computational friendly and general framework for the study of phase transitions in many-body systems.