Gas separation using MOFs

My present research interest focuses on the study of the electronic and structural properties of porous materials such as zeolites and metal organic frameworks (MOFs) for gas separation or sensors by employing DFT, many-body perturbation theory approaches (GW and BSE) and multiconfigurational wave-functioned methods (CASSCF/CASPT2). These methods are combined with classical simulations in collaboration with other groups (see below). Currently, I am working on the understanding and the computational design of materials for carbon capture within two novel families of MOFs: photoresponsive MOFs and spincrossover MOFs. This project was funded by the ANR is soon ending (National Research Agency in France, 2016-2021). It was in collaboration with Dr. Claudio Attaccalite (CNRS Marseille), Dr. Li-Chiang Lin (Ohio State University), Prof. Jihan Kim (KAIST Korea) and Prof. Bess Vlaisavljevich (US South Dakota). To speed up the discovery of materials for efficient gas separation I am interested in employing artificial intelligence techniques (see below).

Optical properties of MOFs

I am interested in studying the optical properties of MOFs for varying applications (optoelectronic, gas separation, CO2 photoreduction).

MOFs and AI

The MIAI is funding a year of postdoc (2021-2020) to work with me and Dr. Emilie Devijver to develop more efficient ML techniques to predict best performer materials for carbon capture. New descriptors will be developed during this project and the vast range of MOF topologies will be studied using a clustering tool. For this we will develop a method that will allow us to compute the carbon capture only for a few relevant MOFs, and generalization properties will ensure good performance to predict capture for the whole dataset. Whereas active learning is a popular area where the machine detects relevant points to be labelled to improve the classifier in a semi-supervised fashion, the challenge in this project is to consider a regression task.