Material Characterisation for CO2 Capture

[Re]generative Quantum Challenge

The issue

The greenhouse effect (GHE) is the main cause of global warming and is the result of several gases present in the atmosphere. The main contribution to the GHE is water vapor, contributing for roughly 2/3 of the total effect. The second greenhouse gas is CO2. Even if other gases have a far greater global warming potential, the sheer amount of CO2 molecules (making up 71.6% [1] of all greenhouse gas emission) makes this gas the prime cause of global warming.


Although the transition of the existing infrastructure from carbon-based sources to cleaner alternatives would be ideal in this regard, such a change requires considerable modifications to the current energy and productions frameworks, especially in hard-to-abate sectors such as heavy industries (cement, steel or chemicals), and many of the proposed technologies are not yet sufficiently developed to facilitate large-scale industrial implementation.


In this regard, carbon capture and sequestration (CCS) technologies that efficiently capture CO2 from existing emission sources will play a vital role until more significant modifications to the current infrastructure can be realized.


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The solution


Membrane technology is considered as one of the most promising energy-efficient way to address the challenge of CO2 adsorption [2]. Among various porous materials, Metal-Organic frameworks (MOFs) have attracted considerable interest for their versatility in gas storage and separation applications. Theoretically one can obtain innumerable chemically distinct nano-porous MOFs, whose properties can be designed for specific applications such as CO2 adsorption, before being tested experimentally.

Although direct computational methods (DFT) are the spearhead for MOF design, recent techniques using machine learning and data-driven methods have shown a strong potential for screening large MOF databases and predict their properties faster than molecular simulations [3].

Very interestingly, the use of topological descriptors such as Persistent Homology (PH) provides interpretable geometric features of the molecule that enhance the accuracy of the prediction of CO2 affinity [4]. This project aims at increasing the accuracy of the prediction of CO2 affinity on a MOF database, by leveraging the geometrical properties of neutral-atom devices.


In particular, the molecular representation of a MOF can be transposed on a neutral-atom array, from which a graph kernel method (QEK)[5] can be applied to recover geometric features (and possibly topologic and homologic features such as the presence of cycles and holes) within the molecule.

[1] Emissions Database for Global Atmospheric Research
[2] Ozkan, Mihrimah, and Custelcean, Radu. The status and prospects of materials for carbon capture technologies. United States: N. p., 2022. Web. doi:10.1557/s43577-022-00364-9
[3] Orhan, I.B., Le, T.C., Babarao, R. et al. Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors. Commun Chem 6, 214 (2023)
[4] Jacob Townsend, Cassie Putman Micucci, John H. Hymel, et al., “Representation of Molecular Structures with Persistent Homology leads to the Discovery of Molecular Groups with Enhanced CO2 Binding”
[5] Boris Albrecht, Constantin Dalyac, Lucas Leclerc, et al., “Quantum feature maps for graph machine learning on a neutral atom quantum processor”, April 2023


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