  Artificial intelligence (AI) is shifting the paradigm of two-phase heat
transfer research. Recent innovations in AI and machine learning uniquely offer
the potential for collecting new types of physically meaningful features that
have not been addressed in the past, for making their insights available to
other domains, and for solving for physical quantities based on first
principles for phase-change thermofluidic systems. This review outlines core
ideas of current AI technologies connected to thermal energy science to
illustrate how they can be used to push the limit of our knowledge boundaries
about boiling and condensation phenomena. AI technologies for meta-analysis,
data extraction, and data stream analysis are described with their potential
challenges, opportunities, and alternative approaches. Finally, we offer
outlooks and perspectives regarding physics-centered machine learning,
sustainable cyberinfrastructures, and multidisciplinary efforts that will help
foster the growing trend of AI for phase-change heat and mass transfer.
