In Search of Covalent Organic Framework Photocatalysts: A DFT‐Based Screening Approach

Abstract

Covalent organic frameworks (COFs) stand out as prospective organic‐based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge‐carrier mobility. The “Clean, Uniform, Refined with Automatic Tracking from Experimental Database” (CURATED) COFs is a database of reported experimental COFs that until now remained mostly unexplored for photocatalysis. In this study, the CURATED COFs database is screened for discovering potential photocatalysts using a set of DFT‐based descriptors that cost‐effectively assesses visible light absorption, preliminary thermodynamic feasibility of the desired pair of redox reactions, charge separation, and charge‐carrier mobility. The workflow can shortlist 13 COFs as prospective candidates for water splitting, and identify materials (Nx‐COF (x = 0–3)) that have been reported as candidates for hydrogen evolution reaction. Overall, the strategy addresses the challenge of exploring a large number of COFs by directing future research toward a selective group of COFs, while providing valuable insights into the structural design for achieving a desired photocatalytic process.

Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning

“…Figure (a) illustrates the case study application of cross-material few-shot learning to predict the band gap values of the COFs calculated by density functional theory (DFT), where only 400 COF band gap data in the CURATED COF database are available. The PMTransformer was fine-tuned to predict the COF band gap values by initializing the weights of the model with the weights obtained from the fine-tuned PMTransformer trained on 20,000 MOF band gaps from the QMOF database.…”

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design

“…Another collection of neural networks was trained by means of a subset of structures (3038 points) from the CoRE MOF 2019 database and their corresponding decomposition temperatures extracted from the TGA data by Nandy et al For the heat capacity prediction, we utilized the values obtained within the harmonic approximation at the GGA level of theory; 214 MOFs from the CoRE MOF 2019 and QMOF database were examined. Henry coefficients of eight gasesN 2 , O 2 , Kr, Xe, CH 4 , CO 2 , H 2 O, and H 2 Scomputed via grand canonical Monte Carlo (GCMC) simulations and the corresponding structures from the QMOF database (1431, 1552, 1297, 1205, 1268, 1538, 1482, and 1352 compounds, respectively) were taken from the data set presented by Jablonka et al PBE band gap values of 61 compounds (from the CURATED COFs database) containing boron or silicon atoms were utilized to estimate the transferability of neural networks of interest. Usable volumetric hydrogen capacity of MOFs under temperature–pressure swing conditions (77 K/100 bar and 160 K/5 bar) was implemented as an objective in the optimization task; 4146 structures from the CoRE MOF 2019 database and GCMC values from the data set presented by Ahmed and Siegel were employed to train an ensemble of CG 2 -SAGE models.…”

Topological Characterization of Metal–Organic Frameworks: A Perspective

“…25 Mournio et al also used CrystalNets to characterize the topology of over 300 COFs as prospective candidates for photocatalysis, showing that the use of this software is not limited to MOFs alone. 91…”