Theoretical and computational modeling are of extreme importance in science, allowing one to underline new physical phenomena and aid in materials-discovery processes. Depending on a particular system of interest (time and spatial scales), different methods can be employed. For example, in this project we aim to study different complex systems from a theoretical and computational point of view, from the use of quantum mechanics (microscopic level) and stochastic approaches (mesoscopic level). However, quantum mechanics approaches like ab initio molecular dynamics based on Density Functional Theory (DFT) allows one to investigate electronic, optical and magnetic properties of a certain material with high precision, but it is limited to hundred of atoms and relatively short simulation time. In this scenario, Machine Learning potentials - Neural Network Force Fields (NNFF) - have an important role, since it provides a way to circumvent these caveats, allowing one to perform simulations that were accurate at the level of DFT for a large system size (up to thousand molecules) and for a long time scale (scale of nanoseconds). Therefore, one of the main goals is to develop NNFF to investigate aqueous systems (water and its solid phases and water/ion) including both electronic and nuclear effects. This means that the simulation will be as realistic as possible and we will be able to make direct comparison to experimental results. On the other hand, the study of stochastic processes became a fundamental part of physics since the original works of Einstein, Langevin and Smulochowski on brownian motion. The major objective of statistical physics is to describe the collective behavior of a large number of objects. In this procedure we try to "forget" particular aspects of different systems and to capture their universal behavior. In this way, we aim to address a series of problems whose fundamental objective is to characterize the dynamics of systems which have a diversity of space and time scales applied, for instance, in mesoscopic systems in out-of-equilibrium. Focusing on a better understanding of growth phenomena described by the Kardar , Parisi (Nobel Prize in 2021) and Zhang (KPZ) equation, which is the simplest nonlinear stochastic equation for growth. Investigations on drug release kinetics encapsulated within polymeric devices and the entropic effects of confined polymer chains.
News published in Agência FAPESP Newsletter about the scholarship: