Speaker
Description
The vast chemical landscape of metal–organic frameworks (MOFs) offers a rich array of compositions, structures, and potential applications.[1] Advancements in Artificial Intelligence (AI) and computer-assisted techniques have not only enhanced MOF discovery but also the field of MOF synthesis.[2] In this presentation, I will offer an experimentalist’s perspective on integrating AI into MOF synthesis and discovery.
In selected examples, I will present closed-loop AI strategies to determine optimal growth conditions for Surface-anchored MOFs (SURMOFs),[3] resulting in enhanced crystallinity, uniform orientation, and low surface roughness.[4] Furthermore, I will discuss how automated data extraction, in combination with machine learning, can be employed to accelerate the synthesis of MOFs.[5] Moreover, the talk will highlight the role of research data management tools in enhancing these data-driven approaches.[6]
References
[1] (a) H. Furukawa et al. Science 2013, 341, 1230444; (b) S. Kitagawa et al. Angew. Chem. Int. Ed. 2004, 43, 2334.
[2] (a) H. Lyu et al. Chem, 2020, 6, 2219; (b) S. M. Moosavi et al. Nat Commun. 2019, 10, 539; (c) P. Z. Moghadam et al. Nat Energy 2024, https://doi.org/10.1038/s41560-023-01417-2; (d) H. Daglar et al. ACS Applied Materials & Interfaces 2022 14, 32134; (e) M. Jalali et al. Nanomaterials 2022, 12, 704; (f) Z. Zheng et al. Angew. Chem. Int. Ed. 2023, 62, e202311983; (g) Y. Luo et al. Adv. Mater. 2019, 31, 1901744.
[3] O. Shekhah et al. Chem. Soc. Rev., 2011, 40, 1081.
[4] (a) L. Pilz et al. Adv. Mater. Interfaces 2023, 10, 2201771; (b) L. Pilz et al. J. Mater. Chem. A, 2023, 11, 24724.
[5] L. Glasby et al. Chemistry of Materials 2023, 35, 4510; (b) P. Kalhor et al. Adv. Funct. Mater. 2024, 2302630; (c) Y. Luo et al. Angew. Chem. Int. Ed. 2022, 61, e202200242;
[6] C-L Lin et al. ChemRxiv. 2023 https://doi.org/10.26434/chemrxiv-2023-2dd4c