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Publication in Nature Chemistry
Kevin, Daniele and Mohamad inspired by one of the life lines of “How to become a millionaire” for a model to predict oxidation states of MOFs.Interested:K. M. Jablonka, D. Ongari, S. M. Moosavi, and B. Smit, Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks Nat Chem (2021) http://dx.doi.org/10.1038/s41557-021-00717-y
Cover of JACS
The perspective of Mohamad and Kevin on a cover of JACS.S. M. Moosavi, K. M. Jablonka, and B. Smit, The Role of Machine Learning in the Understanding and Design of Materials J Am Chem Soc (2020) http://dx.doi.org/10.1021/jacs.0c09105 One out of a million; each data point represents a metal–organic framework used to discover novel materials for carbon (…)
Cover of ACS Central Science
The perspective of Daniele and Leopold on the cover of ACS Central Science.D. Ongari, L. Talirz, and B. Smit, Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution ACS Cent. Sci. (2020) http://dx.doi.org/10.1021/acscentsci.0c00988Evaluating the properties and performances of nanoporous materials from their crystal structure is a long-standing puzzle for (…)
Latest publications
Using collective knowledge to assign oxidation states of metal cations in MOF
K. M. Jablonka, D. Ongari, S. M. Moosavi, and B. Smit, Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks Nat Chem (2021) doi: 10.1038/s41557-021-00717-yAbstract: Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories (…)
Screening MOFs for natural gas purificatiom
Z. Li, Y. Zhang, B. Liu, G. Chen, and B. Smit, Multilevel screening of computation-ready, experimental metal-organic frameworks for natural gas purification AIChE J., e17279 (2021) doi: 10.1002/aic.17279Abstract In this study, traditional Monte Carlo simulation and density functional theory‐based structural optimization methods were combined to screen computation‐ready experimental metal‐organic framework (MOF) database for the application (…)
Smart Carbon Capture with Machine Learning
M. Rahimi, S. M. Moosavi, B. Smit, and T. A. Hatton, Toward smart carbon capture with machine learning Cell Reports Physical Science 2, 100396 (2021) doi: 10.1016/j.xcrp.2021.100396Abstract: Machine learning (ML) is emerging as a powerful approach that has recently shown potential to affect various frontiers of carbon capture, a key interim technology to assist in (…)