Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities

Area of Science:

• Materials Science
• Computational Chemistry
• Artificial Intelligence

Background:

• Predicting thermal properties of nanoporous materials is crucial for applications in thermal insulation and energy storage.
• Nanoporous materials like covalent organic frameworks, metal-organic frameworks, aerogels, and zeolites are key in these applications.
• Current prediction methods face challenges in accuracy and efficiency.

Purpose of the Study:

• To review the application of machine learning (ML) models for predicting thermal properties of nanoporous materials.
• To discuss advancements in ML models, focusing on predictive accuracy and computational efficiency.
• To provide guidelines for researchers on utilizing ML in nanoporous material study and design.

Main Methods:

• Review of literature on ML applications in nanoporous materials.
• Discussion of various ML models including convolutional neural networks, graph neural networks, and physics-informed neural networks.
• Exploration of emerging approaches like multimodal and transfer learning, and interpretable ML.

Main Results:

• ML models show promise in predicting thermal properties, with advancements in accuracy and efficiency.
• Limitations include data availability, maintaining physical consistency, and generalization across material families.
• Emerging techniques like multimodal and transfer learning can reduce computational costs.
• Interpretable ML methods offer insights into underlying physical mechanisms.

Conclusions:

• Machine learning offers powerful tools for predicting and designing nanoporous materials with desired thermal properties.
• Addressing limitations in data and model generalizability is key for broader adoption.
• Interpretable ML enhances understanding and facilitates the development of novel materials for thermal management and energy storage.