Band-Edge Prediction of 2D Covalent Organic Frameworks from Molecular Precursor via Machine Learning
Dayong Wang1, Haifeng Lv1, Yangyang Wan2
1Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Sciences, Key Laboratory of Materials Sciences for Energy Conversion, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), and CAS Center for Excellence in Nanoscience, University of Science and Technology of China, Hefei, Anhui 230026, China.
Abstract:
The band-edge positions of two-dimensional (2D) covalent organic frameworks (COFs) play a crucial role in their applications in photocatalysts and nanoelectronics. However, massive amounts of 2D COFs with targeted band-edge positions from high-level first-principles calculations based on their composition are time-consuming due to the diversity and complexity of unit cell structures. Here, we report a strategy to predict the band-edge positions of 2D COFs by combining first-principles calculations with machine learning (ML). The root-mean-square error (RMSE) of the predicted valence band maximum (VBM) and conduction band minimum (CBM) between ML prediction and first-principles calculated values at the Perdew-Burke-Ernzerhof (PBE) level are 0.229 and 0.247 eV in test data set, respectively. In addition, a linear relationship is established between the PBE results and the HSE06 results with RMSE values of 0.089 and 0.042 eV for VBMs and CBMs in the test data set. Finally, a workflow is developed to determine the band-edge positions of the 2D COFs.
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