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Mapping Out Phase Diagrams with Generative Classifiers.
Mapping Out Phase Diagrams with Generative Classifiers.
- Source :
-
Physical review letters [Phys Rev Lett] 2024 May 17; Vol. 132 (20), pp. 207301. - Publication Year :
- 2024
-
Abstract
- One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states.
Details
- Language :
- English
- ISSN :
- 1079-7114
- Volume :
- 132
- Issue :
- 20
- Database :
- MEDLINE
- Journal :
- Physical review letters
- Publication Type :
- Academic Journal
- Accession number :
- 38829098
- Full Text :
- https://doi.org/10.1103/PhysRevLett.132.207301