# ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning

## TL;DR

This study uses simulations and machine learning to evaluate the gas adsorption and separation capabilities of a large database of hypothetical COFs.

## Contribution

A high-throughput screening method combining GCMC simulations and ML to assess COF performance for gas separations.

## Key findings

- Nitrogen-rich aromatic rings and fluorinated linkers enhance CO2 affinity in COFs.
- Narrow pores and low porosities improve CO2 selectivity in gas separations.
- ML models predict adsorption properties for nearly 25,000 materials from the ReDD-COFFEE database.

## Abstract

In this work, we
performed a high-throughput computational
screening
approach combining Grand Canonical Monte Carlo (GCMC) simulations
and machine learning (ML) to unlock the potential of the ReDD-COFFEE
(Ready-to-use and Diverse Database of Covalent Organic Frameworks
with Force field-based Energy Evaluation) database for gas adsorption
and separation applications. Molecular simulations were first employed
to assess CO2, CH4, H2, N2 and O2 uptakes of acylhydrazone-, azine-, and triazine-based
hypothetical COFs (hypoCOFs). These data were then leveraged to train
ML models capable of predicting adsorption properties for nearly 25000
different types of materials. Adsorption selectivities of ReDD-hypoCOFs
were computed for six important gas separations: CO2/CH4, CO2/H2, CO2/N2, CH4/H2, CH4/N2, and
O2/N2. Structure-performance analyses performed
using molecular fingerprinting on top-selective materials demonstrated
that nitrogen enriched aromatic rings and fluorinated linkers in addition
to narrow pores (<10 Å) and low porosities (<0.7) collectively
strengthen the CO2 affinity of ReDD-hypoCOFs.