Congratulations to our PhD student Elisa Rossi for her contribution to the work "Probing the structural organization of a low temperature transition mixture for CO2 capture through spectroscopic and theoretical studies”. This work focused on understanding the molecular organization of a sorbent made by ethylene glycol, boric acid, and potassium hydroxide, before and after CO2 absorption, combining infrared spectroscopy, differential scanning calorimetry analyses, and computational investigations.
Congratulations to our Postdoctoral researcher Dr. Federica Pes and our PhD student Patrizia Mazzeo for their publication entitled "A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics" in the Journal of Physical Chemistry Letters. The Letter introduces the so-called Quasi Time-Reversible scheme based on Grassmann extrapolation (QTR G-Ext) of density matrices for an accurate calculation of initial guesses in Born–Oppenheimer Molecular Dynamics (BOMD) simulations.
Congratulations to our Ph.D. student Patrizia Mazzeo for winning one of the best poster prizes at EuChemS CompChem 2023, an event organized by the European Chemical Society in Thessaloniki. The title of her poster was "Unraveling the Photoactivation of a Blue Light Using Flavin Photoreceptor through Excited-State SCF Dynamics". The AppA photoreceptor has a pivotal role in the regulation of photosynthesis in bacteria. Its activation is initiated by blue-light absorption, and a complex mechanism which combines electrons and protons transfers follows, leading to the active state.
Congratulations to our PhD students Tommaso Nottoli and Ivan Giannì for their publication entitled "A robust, open‐source implementation of the locally optimal block preconditioned conjugate gradient for large eigenvalue problems in quantum chemistry" in the Journal of Theoretical Chemistry Accounts. They present two open-source implementations of the locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm to find a few eigenvalues and eigenvectors of large, possibly sparse matrices.
