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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. 120 (16), (…)
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
Latest publications
Too Many Materials and Too Many Applications
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.0c00988Abstract: Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new (…)
Charge Separation and Charge Carrier Mobility in Photocatalytic MOFs
M. Fumanal, A. Ortega-Guerrero, K. M. Jablonka, B. Smit, and I. Tavernelli, Charge Separation and Charge Carrier Mobility in Photocatalytic Metal-Organic Frameworks Adv. Funct. Mater. (2020) http://dx.doi.org/10.1002/adfm.202003792Abstract Metal‐organic frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic–organic crystalline materials offer an ideal platform to incorporate light‐harvesting and (…)
Thermoelasticity of Flexible Organic Crystals
J. Maul, D. Ongari, S. M. Moosavi, B. Smit, and A. Erba, Thermoelasticity of Flexible Organic Crystals from Quasi-harmonic Lattice Dynamics: The Case of Copper(II) Acetylacetonate J Phys Chem Lett, 8543 (2020) doi: 10.1021/acs.jpclett.0c02762Abstract: A computationally affordable approach, based on quasi-harmonic lattice dynamics, is presented for the quantum-mechanical calculation of thermoelastic moduli of flexible, stimuli-responsive, (…)