Quantum Materials
Uncovering emergent physical properties of matter arising from quantum effects for functional applications.
This research area in Quantum Materials aims to uncover and harness the emergent properties of matter arising from quantum effects, and to translate these phenomena into functional applications. This interdisciplinary effort spans condensed matter physics, materials science, and biophysics. We investigate the structure and interactions of matter at the atomic scale, with a focus on understanding how complex behaviors emerge. This includes electronic, vibrational, magnetic, and topological properties, with the goal of leveraging them for technologically relevant functionalities such as superconductivity, topological phases, and enhanced chemical reactivity. This activity addresses a broad spectrum of systems, from complex biological systems to advanced materials with near-term technological potential, integrating experimental and theoretical methodologies. These include advanced numerical simulations powered by high-performance computing and modern data-driven techniques. Experimentally, the activity is rooted in electron microscopy, as well as optical and magnetic instrumentation.
Research lines
Advancing the theoretical and computational understanding of quantum phenomena in materials.
This research line develops and applies advanced theoretical and computational methods to understand and predict material properties at the quantum level within condensed matter theory. This includes electronic, magnetic, and vibrational behavior, with emphasis on many-body effects, such as electron–phonon interactions, and symmetry-driven phenomena such as topological states. Special focus is placed on low-dimensional systems, complex magnetism, strongly correlated systems, superconductivity, and nonlinear optical responses, as well as emergent phases under extreme conditions. Methodologically, the approach goes beyond standard density functional theory, combining first-principles methods with advanced numerical techniques enabled by high-performance computing. In-house computational tools for many-body excitations and automated symmetry-based prediction of topological properties are also developed and shared with the scientific community.
Exploring and optimizing functional materials through advanced experimental characterization.
This research line is dedicated to the experimental investigation of novel functional materials, with a particular focus on amorphous and nanocrystalline systems. It aims to understand how composition and processing conditions influence the structure and physical properties of materials. Special attention is given to the emergence of magnetic and multiferroic phases, as well as to optical properties such as selective emission and resonant light-matter interaction. Advanced characterization techniques involving electromagnetic fields and electron beams across a wide temperature range are used to probe their behavior at different length scales, from the fundamental quantum building blocks to the macroscopic behavior. This research is closely connected to technological applications, contributing to the development of materials for sensors, alternative energy sources, and electronics, among others.
Applying computational and data-driven methods to unravel complex biological systems.
This line aims to understand biological systems at molecular resolution and to develop predictive tools for biomedical research. It integrates high-performance computing with molecular dynamics, structural bioinformatics and machine learning, including deep learning approaches, to study biomolecular structure, dynamics and function. Applications include the analysis of protein conformational changes, pathogenic mechanisms, biomolecular recognition and in silico drug design. It also enables benchmarking emerging quantum and quantum-inspired computational methods against classical approaches in realistic biomedical problems, including comparisons between classical machine-learning models and quantum machine-learning strategies for predicting the pathogenicity of genetic mutations. It connects computational biophysics, AI-assisted molecular medicine, and the methodological frontier of quantum-enabled data analysis.