Phase Transition Study Meets Machine Learning
Y. G., Long-Gang Pang, Rui Wang, Kai Zhou
Abstract
In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.
Topics & Concepts
Phase transitionTransition (genetics)Focus (optics)Computer scienceArtificial intelligenceCognitive sciencePsychologyPhysicsChemistryCondensed matter physicsOpticsGeneBiochemistryQuantum, superfluid, helium dynamicsSuperconducting Materials and ApplicationsNuclear physics research studies