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DOI: 10.4121/5866cc9a-78bf-4a0c-9280-ae526da86ac9

Thakolkaran, Prakash; Zheng, Yiwen; Guo, Yaqi; Vashisth, Aniruddh; Kumar, Siddhant (2025): Data and code underlying the publication: Deep learning reveals key predictor of thermal conductivity of covalent organic frameworks. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/5866cc9a-78bf-4a0c-9280-ae526da86ac9.v1

Categories

• Artificial Intelligence and Image Processing
• Atomic, Molecular, Nuclear, Particle and Plasma Physics
• Physical Sciences
• Information and Computing Sciences

Keywords

Covalent organic frameworks Deep Learning Thermal conductivity Transformer Neural Network

Licence

MIT

Interoperability

• RO-Crate Metadata

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by Prakash Thakolkaran , Yiwen Zheng , Yaqi Guo , Aniruddh Vashisth , Siddhant Kumar

Abstract

The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their structure and thermal properties remains poorly understood. Analysis of a dataset containing over 2,400 COFs reveals that conventional features such as density, pore size, void fraction, and surface area do not reliably predict thermal conductivity. To address this, an attention-based machine learning model was trained, accurately predicting thermal conductivities even for structures outside the training set. The attention mechanism was then utilized to investigate the model's success. The analysis identified dangling molecular branches as a key predictor of thermal conductivity, leading us to define the dangling mass ratio (DMR), a descriptor that quantifies the fraction of atomic mass in dangling branches relative to the total COF mass. Feature importance assessments on regression models confirm the significance of DMR in predicting thermal conductivity. These findings indicate that COFs with dangling functional groups exhibit lower thermal transfer capabilities. Molecular dynamics simulations support this observation, revealing significant mismatches in the vibrational density of states due to the presence of dangling branches.

Please refer to the README.md file for more information on the code and data.

History

• 2025-10-02 first online, published, posted

Publisher

4TU.ResearchData

Format

.py, .ipynb, .csv, .yml, .cif, .txt

Organizations

TU Delft, Faculty Mechanical Engineering, Department of Materials Science and Engineering;
University of Washington, Department of Mechanical Engineering