Materials design by evolutionary optimization of functional groups in metal-organic frameworks.

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Metal-organic frameworks (MOFs) are promising materials for gas storage and separation.
• Optimizing MOFs for specific properties like CO2 uptake is crucial for applications such as carbon capture.
• Existing methods for MOF optimization can be computationally intensive and time-consuming.

Purpose of the Study:

• To develop and apply a genetic algorithm for efficient optimization of MOF properties.
• To enhance the CO2 uptake capacity of experimentally characterized MOFs for post-combustion capture.
• To identify novel MOF structures with high CO2 adsorption performance.

Main Methods:

• Development of a genetic algorithm to evolve functional groups within MOF pores.
• Screening of a vast chemical space (1.65 trillion structures) of MOF derivatives.
• Evaluation of CO2 uptake capacity under relevant post-combustion conditions (0.15 atm, 298 K).

Main Results:

• Identification of 1035 MOF derivatives from 23 parent structures with CO2 uptake >3.0 mmol/g.
• Significant improvements in CO2 adsorption, with some MOFs (e.g., MIL-47) showing over 400% increase.
• Optimization of 141 experimentally characterized MOFs, demonstrating the algorithm's practical applicability.

Conclusions:

• The developed genetic algorithm provides an efficient route to discover MOFs with superior CO2 capture capabilities.
• The identified high-performing MOF structures serve as valuable targets for experimental synthesis and validation.
• This approach accelerates the discovery of advanced materials for carbon capture technologies.