As a researcher, my motivation is to contribute to the understanding of intermolecular interactions to accurately predict and model complex fluid dynamic behaviors, operating at the intersection of chemistry, statistical mechanics, and fluid dynamics.
First, I started evaluating fluid dynamics problems at the nanoscale while I pursued my PhD in Engineering Sciences at Universidad de Concepción (Chile), with co-supervision from the Technical University of Denmark. Later, I moved to Melbourne, Australia, to hold a postdoctoral position at Swinburne University of Technology. There, I improved my programming skills by writing electrostatic potentials with multipole expansion and dipole polarization employing the Ewald summation technique. Later, I moved to Denmark to work as a postdoc under the supervision of Frank Jensen at Aarhus University. This project focused on the development of a charge-flow polarization model, transferring information from ab initio computations to classical MD simulations.
Currently, I am a postdoctoral researcher at UiO supported by the DSTrain MSCA COFUND Fellowship. My goal is to integrate my previous expertise with the recent advances in machine learning techniques. In this project, we will leverage Machine Learning Force Fields trained on quantum mechanical data to model and unlock insights into complex fluid dynamics problems within a geological context.