Welcome!
I am a PhD researcher at the University of Bremen, trying to push the boundaries of computational materials science to tackle the grand-challenges of our time, through technological innovation and scientific breakthroughs.
My research in a nutshell
I focus on the development of cutting-edge atomistic simulation methods, including AI, machine-learning, and high-throughput techniques, and their application to a wide range of materials science problems. Recently, I started working on developing sustainable and green metal-extraction processes to revolutionize how we produce and recycle critical metals from raw materials and waste.
During my PhD I developed several open-source packages based on the AiiDA infrastructure able to streamline the calculation of (i) phonons, infrared, and Raman spectra with arbitrary density-functional theory (DFT) functional, (ii) self-consistent Hubbard parameters from first principles, (iii) active-learning training of state-of-the-art machine-learning interatomic potentials, and (iv) metal electrowinning phase diagrams in molten salts electrolytes. These codes enable robust phase-stability predictions, corrosion phase diagrams, and thermodynamic properties at realistic temperatures and pressures with DFT accuracy.