Prof. Rodrigo Freitas

Rodrigo has leveraged data-science tools to perform physics-based modeling of materials by blending prevailing scientific methods with Machine Learning, producing physically-consistent models and conceptual knowledge. His overarching goal is to employ AI methods as an aid to augment human intuition, rather than a substitute thereof.

Professor Rodrigo employs a range of computational techniques in his research, including atomistic simulation methods (molecular dynamics and free-energy calculations), quantum mechanical calculations (density functional theory and path-integral molecular dynamics), and Machine Learning tools.

Research focus is on elucidating the fundamental mechanisms of microstructural evolution for systems of relevance in materials science broadly construed. His research group will employ a range of computational techniques with the goal of bridging the gap between the all-atom information gathered from simulations and the mesoscale description of microstructural elements employed in materials science.