Category: News
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 (…)
Perspective in Nature Chemistry: Pancakes and Chemical Data
See the press release: https://actu.epfl.ch/news/chemical-data-management-an-open-way-forward-6/ Kevin and Luc have written their vision on Open Science and Chemical Data in: K. M. Jablonka, L. Patiny, and B. Smit, Making the collective knowledge of chemistry open and machine actionable Nat Chem 14 (4), 365 (2022) http://dx.doi.org/10.1038/s41557-022-00910-7
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
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.0c00988
Sauradeep wins the world finals of FameLab
Here is his winning presentation:
Sauradeep wins Swiss Finals of FameLab
You can find an interview with Sauradeep here: https://actu.epfl.ch/news/sauradeep-majumdar-winner-of-famelab-switzerland-2/? See here his winning presentation for the Swiss edition of FameLab
Cover of Chem Rev
MOFs are made for big-data science: The neurons of artificial neural networks, representing the big-data approach to chemistry, become one with the framework of a metal-organic framework (figure by Alexander Tokarev) Ref: K. M. Jablonka, D. Ongari, S. M. Moosavi, and B. Smit, Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Chem. Rev. (…)
Chemical Reviews: Machine learning and MOFs
The recent review of Kevin, Mohamad, and Daniele can be found here: K. M. Jablonka, D. Ongari, S. M. Moosavi, and B. Smit, Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Chem Rev (2020) http://dx.doi.org/10.1021/acs.chemrev.0c00004
Andres’s work on the cover of Chem Mat
A porphyrin ruthenium-based photocatalytic active MOF is studied by ab initio calculations. Photoinduced electron transfer from the porphyrin to the ruthenium occurs upon light absorption, leading to charge separation. Ref: Chem. Mat. 32 (10), 4194 (2020) doi: 10.1021/acs.chemmater.0c00356