About
I am a PhD Candidate in Computational Physics at the Polytechnic University of Catalonia and a visiting Researcher at the University of Tokyo. My research focuses on Computational Materials Science, where I use first-principles atomistic modeling and machine learning techniques, such as Density Functional Theory, Machine Learning Interatomic Potentials, and Graph Neural Networks, to understand the physics and chemistry of materials. I am also interested in developing responsible AI tools to accelerate materials discovery and property prediction.
My current research primarily revolves around two topics. First, I am deeply interested in how temperature influences the optoelectronic properties of semiconductors. In particular, I have investigated this effect in anharmonic perovskite systems using dynamical approaches, revealing significant band gap renormalization driven by polar electron-phonon coupling. These findings could enable technologies where optoelectronic properties are tunable through temperature or external fields. I am also developing machine learning frameworks to study thermal optoelectronic effects in more complex materials, such as solid solutions and molecular crystals.
My second main interest lies in the creation of high-quality datasets for training machine learning models in materials science. In addition, my research experience spans various topics, including computational screening of materials for energy applications, machine learning-based crystal structure prediction, and the study of phase competition in systems with multiple metastable states.
Throughout my brief research career, I have worked in leading institutions across Spain, the United Kingdom, and Japan, collaborating in highly international and intellectually stimulating environments. I have presented my work at major international conferences, such as MRS (USA), EMRS (France), and AI4X (Singapore), and have authored and co-authored publications in high-impact journals, such as the Journal of the American Chemical Society.
