Computational Quantum Materials

In our research group, we study and model quantum materials using first-principles methodologies based on accurate numerical solutions of the many-body Schrödinger equation. These materials exhibit complex interactions among electronic, spin, orbital, and lattice degrees of freedom, leading to emergent phenomena such as Mott insulators, Lifshitz transitions, polarons, non-collinear magnetic phases, multiferroic states, and phases with topological properties—phenomena that cannot be explained using semiclassical approaches.

At the core of our research is the integration of advanced computational techniques (DFT, DFT + U, hybrid functionals, GW, Bethe-Salpeter, ab initio molecular dynamics, quantum Monte Carlo, functional renormalization group) with tools from machine learning, artificial intelligence, and data science. These approaches, part of the materials informatics paradigm, accelerate the discovery and analysis of new phases of matter by identifying patterns in complex datasets and building predictive models of electronic and structural properties. The synergistic use of theoretical physics and AI enables efficient exploration of the materials phase space, offering new perspectives for the design of quantum materials with tailored functionalities.