Azade is a new post-doctoral fellow in the group of Prof. Dr. Maria Fyta, where she will focus on catalysis in confined spaces, as well as the use and implementation of data-driven methods.
Her true passion lies in the use of computational methods in materials science and computational chemistry. She has worked with various computational methods, including ab initio and classical molecular dynamics (AIMD and MD), as well as enhanced sampling methods and free energy calculations (well-tempered metadynamics or WT-MTD).
During her master studies in the Sharif University of Technology (Tehran, Iran), she investigated the crystallization and glass transition of nanoparticles using the molecular dynamics approach. For her Ph.D. in EPFL (Lausanne, Switzerland), she worked on a multi-disciplinary project bringing materials science and biology together, where the objective was to understand the atomistic mechanism of events, which trigger or lead to the nucleation and growth of hydroxyapatite (a calcium-phosphate mineral), on a rutile surface, and the effect of ionic and organic species on such phenomena. In her recent post-doctoral position in the Max Planck Institute for Polymer Research (Mainz, Germany), she implemented Machine Learning and developed a DNN (Deep Neural Network) to interpret and predict experimental findings on the Raman spectra of water.
Her research interests revolve around atomistic modelling approaches, surfaces, bio-relevant systems, and data-driven methods.