Making Molecules Vibrate
K. M. Jablonka, L. Patiny, and B. Smit, Making Molecules Vibrate: Interactive Web Environment for the Teaching of Infrared Spectroscopy J. Chem. Educ. (2022) doi: 10.1021/acs.jchemed.1c01101. Abstract: Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique because of its ability to identify functional groups and its ease of use. Interestingly, it allows the (…)
Diversifying Databases of MOFs for High-Throughput Computational Screening
S. Majumdar, S. M. Moosavi, K. M. Jablonka, D. Ongari, and B. Smit, Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening ACS Appl. Mater. Interfaces (2021) doi: 10.1021/acsami.1c16220 Abstract: By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases (…)
Optimal Photocatalytic Hydrogen Generation from Water Using Pyrene-Based MOF
F. P. Kinik, A. Ortega-Guerrero, F. M. Ebrahim, C. P. Ireland, O. Kadioglu, A. Mace, M. Asgari, and B. Smit, Toward Optimal Photocatalytic Hydrogen Generation from Water Using Pyrene-Based Metal–Organic Frameworks ACS Appl. Mater. Interfaces (2021) doi: 10.1021/acsami.1c16464 Abstract: Metal–organic frameworks (MOFs) are promising materials for the photocatalytic H2 evolution reaction (HER) from water. To find (…)
Trends in Atomistic Simulation Software Usage
L. Talirz, L. M. Ghiringhelli, and B. Smit, Trends in Atomistic Simulation Software Usage [Article v1.0] Living J. Comp. Mol. Sci. 3 (1) (2021) doi: 10.33011/livecoms.3.1.1483 Abstract: Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continuous rise. This (…)
Can we screen millions of structures for cost-effective carbon capture?
C. Charalambous, F. Mcilwaine, E. Moubarak, B. Smit, and S. Garcia, Can we systematically screen millions of chemical structures for cost-effective carbon capture? Boletín del Grupo Español del Carbón 61, 14 (2021) Abstract: Finding the optimal solid adsorbent to capture CO2 for a given source of CO2 and sink (destination) of CO2 is an interesting (…)
Common workflows for computing material properties using different quantum engines
S. P. Huber, E. Bosoni, M. Bercx, J. Bröder, A. Degomme, V. Dikan, K. Eimre, E. Flage-Larsen, A. Garcia, L. Genovese, D. Gresch, C. Johnston, G. Petretto, S. Poncé, G.-M. Rignanese, C. J. Sewell, B. Smit, V. Tseplyaev, M. Uhrin, D. Wortmann, A. V. Yakutovich, A. Zadoks, P. Zarabadi-Poor, B. Zhu, N. Marzari, and G. (…)
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
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-y Abstract: 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 (…)
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.17279 Abstract 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 (…)
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.2021.100396 Abstract: 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 (…)