The discovery of advanced thermal materials with exceptional phonon properties drives technological advancements, impacting innovations from electronics to superconductors. Understanding the intricate relationship between composition, structure, and phonon thermal transport properties is crucial for speeding up such discovery. Exploring innovative materials involves navigating vast design spaces and considering chemical and structural factors on multiple scales and modalities. Artificial intelligence (AI) is transforming science and engineering and poised to transform discovery and innovation. This era offers a unique opportunity to establish a new paradigm for the discovery of advanced materials by leveraging databases, simulations, and accumulated knowledge, venturing into experimental frontiers, and incorporating cutting-edge AI technologies. In this perspective, first, the general approach of density functional theory (DFT) coupled with phonon Boltzmann transport equation (BTE) for predicting comprehensive phonon properties will be reviewed. Then, to circumvent the extremely computationally demanding DFT + BTE approach, some early studies and progress of deploying AI/machine learning (ML) models to phonon thermal transport in the context of structure–phonon property relationship prediction will be presented, and their limitations will also be discussed. Finally, a summary of current challenges and an outlook of future trends will be given. Further development of incorporating AI/ML algorithms for phonon thermal transport could range from phonon database construction to universal machine learning potential training, to inverse design of materials with target phonon properties and to extend ML models beyond traditional phonons.
This content will become publicly available on March 4, 2025
- Award ID(s):
- 2317008
- NSF-PAR ID:
- 10534580
- Editor(s):
- Grein, Christoph
- Publisher / Repository:
- American Institute of Physics (AIP)
- Date Published:
- Journal Name:
- Applied physics letters
- Volume:
- 124
- Issue:
- 10
- ISSN:
- 0003-6951
- Page Range / eLocation ID:
- 102202-1-6
- Subject(s) / Keyword(s):
- Vibrational properties, Potential energy surfaces, Electrical conductivity, Phonons, Thermodynamic properties, Deep learning, Machine learning, Crystalline solids, Interatomic force constants, Interatomic potentials
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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