UCL Department of Chemical Engineering


Computer simulations speed up the discovery of MOF adsorbents for CO2 capture and hydrogen storage

29 January 2024

Metal–organic frameworks (MOFs) are a class of nano-porous materials and are promising for a many energy technologies including carbon capture and hydrogen storage. 

MOFs are nanoporous materials that allow for the storage or capture of gases

MOFs consists of Lego-like molecular building blocks and by mixing and combining different blocks, a wide range of structures can be synthesised: in fact, we have identified more 100,000 MOF-like materials in the Cambridge Structural Database (CSD). Because of this large number of structures, an important challenge arises: how do we rapidly identify promising MOFs for a given gas adsorption or separation application? Clearly, experimental trial-and-error and quantum calculations performed on 1000s of materials are time-consuming, expensive, and at times, not feasible. Essentially, we are dealing with a typical problem of finding a needle in a haystack when it comes to looking for top-performing MOF adsorbents.

In this article published in Nature Energy, Dr Peyman Z. Moghadam, Associate Professor from the Chemical Engineering Department at University College London, Dr Yongchul G. Chung from Pusan National University, and Professor Randall Snurr from Northwestern University, review how molecular simulations and machine learning can rapidly screen hundreds of thousands materials to significantly speed up the way MOFs are discovered for energy applications including CO2 capture and hydrogen storage. 

Dr Moghadam says that “Development of new materials is critical for many problems related to energy and the environment, and nowadays, we see a number of MOFs (among other nanomaterials), that are developed or discovered on the computer, before they are made in laboratory for their application.  In the coming years, machine learning will play a major role in accurately describing gas-MOF interactions, and self-driven digital laboratories can record tremendous amount of data to augment collaboration between computational and experimental MOF chemists, and to advance the field of data-driven materials.


Image credit

Dr Peyman Z. Moghadam