Accelerated discovery of porous materials for carbon capture by machine learning: A review

Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation

“…354 Zhang elucidated the workflow of applying ML to tackle problems in materials design and further highlighted the challenges in the existing works. 355 Yang et al developed automated ML establishing a link between structural features and properties of COF materials. 280 ML can also assist in screening the adsorbents and this can also speed up selection process and can avoid trial and error experimental process.…”

Spatial tuning of adsorption enthalpies by exploiting spectator group effects in organosilica carbon capture materials

“…These regions would then be connected to different DH ads (CO 2 ) values. So-called functional gradient materials (FGMs) are expected to fulll more complex tasks compared to homogeneous materials as discussed in the nice review article by Pragya et al 29 FGMs have also been proposed for their large potential in carbon capture applications, 3,[30][31][32][33] but there are only a few experimental realizations yet. We could already show that organosilica materials containing chemical and/or structural gradients can be prepared using click-chemistry.…”

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space

“…Therefore, it is generally perceived that the effective elimination of GHGs is the prerequisite to alleviate global warming. Nanoporous materials, − which include a variety of substances such as activated carbon, zeolite, covalent organic frameworks (COFs), and metal–organic frameworks (MOFs), − are commonly used as adsorbents or catalysts. In 1977, MOFs were interpreted as a type of nanomaterial with porous network-like structural features formed by linking inorganic metal units (e.g., metal ions, clusters, or chains) and organic ligands (carboxylates, phosphates, nitrogen-containing ligands, etc.).…”