Machine Learning for Thermal Transport
Thermal transport is a fundamental process that plays a key role in many applications, such as thermal management of electronic devices, energy conversion (e.g., thermoelectrics), and thermal insulation. Remarkable progress has been made in understanding and engineering thermal transport in recent decades across different scales. However, many challenges still exist, particularly for complex heat transfer problems. Machine learning is emerging as a powerful tool to tackle problems that are difficult to solve using traditional analytical, computational, or experimental approaches. This special topic aims to provide a timely forum for presenting and discussing the most recent advances in the development and application of machine learning to study thermal transport in applied physics.
Topics covered include, but are not limited to:
- Prediction of thermal properties using machine learning
- Development of machine learning interatomic potentials for thermal transport simulations
- Machine learning-guided discovery of thermal materials
- Design of metamaterials for thermal applications by machine learning
- Machine learning for thermal energy storage and conversion
- Machine learning for thermal convection applications
- Machine learning for thermal radiation applications
- Machine learning for multi-phase heat transfer applications
- Novel descriptors for describing thermal transport
- Data acquisition, standardization, and database construction
- Physics-informed machine learning approaches for solving thermal transport equation
- Machine learning-based data analysis for thermal measurements
Guest Editors
Ruiqiang Guo, Shandong Institute of Advanced Technology
Bing-Yang Cao, Tsinghua University
Tengfei Luo, University of Notre Dame
Alan McGaughey, Carnegie Mellon University
Submission and acceptance criteria:
Manuscripts considered for publication in Journal of Applied Physics are expected to meet the journal’s standard of acceptance: to report on original and timely results that significantly advance understanding in contemporary applied physics. Manuscripts submitted for consideration in this Special Topic must meet the same criteria and will undergo the journal’s standard peer-review process. The Editorial Team of Journal of Applied Physics will issue final decisions on the submitted manuscripts. Manuscripts will publish immediately upon acceptance.
For more information on the journal’s editorial policies, please click here.
Manuscripts must be submitted through the online submission system (PXP) of Journal of Applied Physics. Please select the Special Topic “Machine Learning for Thermal Transport” to submit your manuscript for consideration in this Special Topic.