Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant
K. M. Jablonka, C. Charalambous, E. Sanchez Fernandez, G. Wiechers, J. Monteiro, P. Moser, B. Smit, and S. Garcia, Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant Sci Adv 9 (1), eadc9576 (2023) doi: 10.1126/sciadv.adc9576 Abstract: One of the main environmental impacts of amine-based carbon capture processes is the emission of (…)
How to Decarbonize Our Energy Systems: Process-Informed Design of New Materials for Carbon Capture
S. Garcia and B. Smit, How to Decarbonize Our Energy Systems: Process-Informed Design of New Materials for Carbon Capture Chem Ing Tech (2023) doi: 10.1002/cite.202200179 Abstract: Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO2 sources and integrated into an equally diverse range (…)
Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF
N. P. Domingues, S. M. Moosavi, L. Talirz, K. M. Jablonka, C. P. Ireland, F. M. Ebrahim, and B. Smit, Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF Commun Chem 5 (1), 170 (2022) doi: 0.1038/s42004-022-00785-2 Abstract: The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always (…)
J. Young, F. McIlwaine, B. Smit, S. Garcia, and M. van der Spek, Process-informed adsorbent design guidelines for direct air capture Chem Eng J, 141035 (2022) doi: 10.1016/j.cej.2022.141035 Abstract: Direct air capture using solid adsorbents is a proven technology critical to reducing our net greenhouse gas emissions to zero and beyond. Currently, academic research into the (…)
Mechanically robust mesoporous merged-net MOFs
H. Jiang, S. M. Moosavi, J. Czaban-Jóźwiak, B. Torre, A. Shkurenko, Z. O. Ameur, J. Jia, N. Alsadun, O. Shekhah, E. Di Fabrizio, B. Smit, and M. Eddaoudi, Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks Matter-Us (2022) doi: 10.1016/j.matt.2022.10.004 Abstract: Access to metal-organic frameworks (MOFs) with enhanced mechanical stability (…)
Publication in Nature Materials
A subtitle of this work: Machine learning without data! S. M. Moosavi, B. Á. Novotny, D. Ongari, E. Moubarak, M. Asgari, Ö. Kadioglu, C. Charalambous, A. Ortega-Guerrero, A. H. Farmahini, L. Sarkisov, S. Garcia, F. Noé, and B. Smit, A data-science approach to predict the heat capacity of nanoporous materials Nat Mater (2022) http://dx.doi.org/10.1038/s41563-022-01374-3 (…)
A data-science approach to predict the heat capacity of nanoporous materials
S. M. Moosavi, B. Á. Novotny, D. Ongari, E. Moubarak, M. Asgari, Ö. Kadioglu, C. Charalambous, A. Ortega-Guerrero, A. H. Farmahini, L. Sarkisov, S. Garcia, F. Noé, and B. Smit, A data-science approach to predict the heat capacity of nanoporous materials Nat Mater (2022) doi: 10.1038/s41563-022-01374-3 Abstract: The heat capacity of a material is a (…)
SELFIES and the future of molecular string representations
M. Krenn, Q. Ai, S. Barthel, N. Carson, A. Frei, N. C. Frey, P. Friederich, T. Gaudin, A. A. Gayle, K. M. Jablonka, R. F. Lameiro, D. Lemm, A. Lo, S. M. Moosavi, J. M. Nápoles-Duarte, A. Nigam, R. Pollice, K. Rajan, U. Schatzschneider, P. Schwaller, M. Skreta, B. Smit, F. Strieth-Kalthoff, C. Sun, G. (…)
Reply to “Inconsistencies in the specific nucleobase pairing motif prone to photodimerization in a MOF nanoreactor”
S. L. Anderson, P. G. Boyd, A. Gładysiak, T. N. Nguyen, R. G. Palgrave, D. Kubicki, L. Emsley, D. Bradshaw, M. J. Rosseinsky, B. Smit, and K. C. Stylianou, Reply to “Inconsistencies in the specific nucleobase pairing motif prone to photodimerization in a MOF nanoreactor” Nat Commun 13 (1), 4486 (2022) doi: 10.1038/s41467-022-30193-y REPLYING TO (…)
xtal2png: A Python package for representing crystal structure
Sterling G. Baird, Kevin M. Jablonka, Michael D. Alverson, Hasan M. Sayeed, Mohammed Faris Khan, Colton Seegmiller, Berend Smit, and T. D. Sparks, xtal2png: A Python package for representing crystal structure as PNG files J. Open Source Softw. 7 (76), 4528 (2022) doi: 10.21105/joss.04528 Abstract: The latest advances in machine learning are often in natural language (…)