Active-MOF

This project seeks to upgrade our top-notch automated high-throughput synthesis robot to double its screening capacity. It is directly linked to the Active-Multi-MOF ChemAI project (PhD Grant funded by PSL ChemAI). Active-Multi-MOF project aims to develop a data-driven approach for optimizing the synthesis of multivariate metal-organic frameworks (MOFs) using active learning methods to drive high-throughput robotic synthesis. To date, the vast majority of MOF discovery methods relies on serendipity, with computational screening unable to predict synthesizability or control synthesis conditions. By integrating experimental data acquisition with machine learning, the project aims to systematically explore the effect of synthesis parameters and help shorten the MOF discovery loop. Bayesian optimization will guide experiment selection, minimizing costs and maximizing insights. We will develop this methodology on a specific family of materials: multivariate (MTV) MOFs. In fact, Mixed-ligand or mixed-metal MOFs can offer enhanced adsorption, separation or catalytic activity over their parent compounds, but are difficult to characterize due to their structural complexity and/or the need to apply careful activation processes. Focusing on machine learning-driven optimization, this research will leverage existing infrastructure and collaboration, ensuring feasibility within a PhD timeline. This innovative integration of active learning and robotics offers a more systematic approach to materials discovery, focusing on efficiency in terms of both data and chemicals.