People

Marcos Calegari Andrade

Marcos graduated in chemistry from the University of Sao Paulo and got his PhD  from Princeton University, under supervision of Prof. Annabella Selloni. While at Princeton, he developed machine learning models to simulate the chemistry and vibrational spectroscopy of condensed phase systems, with focus on the surface chemistry of photocatalysts in contact with liquid water. He then joined the Quantum Simulations Group at Lawrence Livermore National Lab, where he applied deep neural network models to investigate the fundamental science of systems used for climate and energy securities, including water at extreme confinement, direct air capture of carbon dioxide, photovoltaics and hydrogen production. His current research at UCSC focuses on the application of machine learning to molecular simulations, with topics related to the mechanism of chemical reactions, vibrational spectroscopy and code development for automated simulations. Marcos’ academic recognitions include the Sokol excellence in teaching award from Princeton University, and the ACS Chemical Computing Group Excellence Award.

Email: mcalegar@ucsc.edu

Google Scholar page

Feiteng Wang

My research interest is mainly on correlating the interfacial structure and dynamics at solid/liquid interfaces with the observables using machine learning molecular dynamics. For example, I have revealed how the correlation of the desorption events at Pt/water interfaces accelerates water exchange dynamics and also showed how the orientation of water molecules at Pt211/water interface leads to anisotropic water dynamics.

Filippo Balzaretti

Filippo transitioned from a pure mathematical background to pursuing a PhD in Physics and Electrical Engineering at the University of Bremen, Germany. There, he learned to use Density Functional Theory (DFT) to analyze how water pollutants interact with TiO2 catalytic surfaces under both dark and light conditions. During his studies, he also explored alternative and complementary methods to DFT, such as classical force fields and Density Functional Tight Binding (DFTB). The latter particularly caught his interest and, in his first postdoctoral position at Stanford University / SLAC National Accelerator Laboratory, Filippo developed new DFTB parametrizations to enable quick and accurate calculations of the electronic properties in transition metal catalysts.
Currently, as part of Marcos Calegari’s group, Filippo combines machine learning with tight-binding methods to enhance the connection between theoretical models and real-world experiments in catalysis and materials science.