Last week on June 9th and 10th, dr. Sanggyu Chong visited the CMM to exchange ideas in small-group and one-on-one discussions with us. Dr. Chong recently started working as a postdoctoral researcher with prof. Michele Ceriotti at the Laboratory of Computational Science and Modeling (COSMO) at the École Polytechnique Fédérale de Lausanne (EPFL), focusing on machine learning potentials for MOFs and other materials. Before, he completed his PhD at KAIST (Korea Advanced Institute of Science and Technology), working on the electronic structure modelling of MOFs under the supervision of prof. Jihan Kim.
During his research visit to the CMM Sanggyu gave a talk about his research on the ‘Computational Design of Electrically Conductive Metal-Organic Frameworks by Exploring Strategies to Construct Charge Transport Pathways’. Metal–organic frameworks, or MOFs, which are widely recognized for their ultrahigh porosity and chemical tunability, have shown great promise in a variety of adsorption-based applications. More recently, it has been shown that electrical conductivity can be induced in MOFs, which has led to their successful utilization in electronic or electrochemical applications. One could expect that chemical tunability and high porosity of MOFs would allow these materials to achieve outstanding and unprecedented performances in these new application fields. Nonetheless, progress is stunted by the limited number of electrically conductive MOFs discovered to date, and hence continued discovery of new and improved electrically conductive MOFs is crucial. In his talk, Sanggyu discussed the computational design approaches for the discovery of new electrically conductive MOFs by constructing long-range charge transport (CT) pathways in the materials. More specifically, he presented on the following: (1) rational modifications of previously insulating MOFs to newly induce electrical conductivity, (2) installation of electroactive moieties in MOFs with significant framework flexibility, (3) topologically guided construction of 3D π-d conjugated MOFs, and (4) development of a deep learning model to predict the electronic structures of MOFs. We are eager to witness how the design approaches and computational methods proposed and developed by Sanggyu will significantly expedite the discovery of new electrically conductive MOFs!
