SoftwareApplication
name: Data and code underlying the publication: Deep learning reveals key predictor of thermal conductivity of covalent organic frameworks
description: Data and code underlying the publication: Deep learning reveals key predictor of thermal conductivity of covalent organic frameworks
description: 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.
author: https://orcid.org/0000-0002-5836-6588
author: https://orcid.org/0000-0002-6908-3436
author: 0009-0008-1098-445X
author: https://orcid.org/0000-0002-4740-1296
author: https://orcid.org/0000-0003-1602-8641
keywords: Covalent organic frameworks
keywords: Transformer Neural Network
keywords: deep learning
keywords: Thermal conductivity
license:
  creative work: creativeWork
    name: MIT
reference publication: d5dd00126a

https://orcid.org/0000-0002-5836-6588
type: Person
given name: Prakash
family name: Thakolkaran
name: Prakash Thakolkaran

https://orcid.org/0000-0002-6908-3436
type: Person
given name: Yiwen
family name: Zheng
name: Yiwen Zheng

0009-0008-1098-445X
type: Person
given name: Yaqi
family name: Guo
name: Yaqi Guo

https://orcid.org/0000-0002-4740-1296
type: Person
given name: Aniruddh
family name: Vashisth
name: Aniruddh Vashisth

https://orcid.org/0000-0003-1602-8641
type: Person
given name: Siddhant
family name: Kumar
name: Siddhant Kumar

d5dd00126a
type: ScholarlyArticle
name: Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks
pagination: 3351-3362
url: https://doi.org/10.1039%2Fd5dd00126a