In our research group, we are passionate about research in materials for new clean energy technologies, as well as materials that could enable new technologies. Recently, we have worked on areas such as redox flow batteries (RFBs), lithium ion batteries (LIBs), organic light-emitting diodes (OLEDs), organic semiconducting laser diodes (OSLDs), reticular frameworks (e.g., MOFs, COFs), catalysis, electrocatalysis (e.g., OER), sodium ion batteries (SIBs), thermoelectrics (TE), 2D materials, and photovoltaic devices (PVs).
We develop computational tools to produce, manage, and store large amounts of data from calibrated electronic calculations, allowing rapid navigation of the chemical space. We implement machine learning methods developed in the group to couple with closed-lop and high-throughput virtual screening workflows. For instance, we are pioneers of the use and development of generative models and
genetic algorithms for inverse design of molecules and materials, which allow a smart search of the chemical space based on desired properties/functionalities. Also, we apply active learning approaches for the optimization of experimental properties.
This guided search of molecules can be used as a starting point for our automated robotic
platforms (such as our Chemspeed system) to operate in a closed-loop approach for the synthesis of new materials.