How AI can accelerate Materials Discovery for CO2 Capture

The application of Artificial Intelligence (AI) techniques to chemistry and materials science has led to a significant advancements in recent years. In this presentation, I will provide an overview of our team’s work in the application of AI materials discovery in the context of chemical separations. One application is the automated discovery of polymer membranes for carbon dioxide filtration [1]. Another example is AI-enabled design and screening of reticular materials, which are promising candidates in carbon dioxide capture [2]. Finally, I will outline how we explore the potential of quantum computing within computational discovery workflows [3].

References

[1] Giro, R., et al. AI powered, automated discovery of polymer membranes for carbon capture. npj Comput Mater 9, 133 (2023). https://doi.org/10.1038/s41524-023-01088-3 [2] Oliveira, F.L., et al. CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks. Sci Data 10, 230 (2023). https://doi.org/10.1038/s41597-023-02116-z [3] M. A. Barroca, et al., APL Quantum 1, 046103 (2024). Exploring qubit-ADAPT-VQE for materials discovery in direct air capture. https://doi.org/10.1063/5.0219500