Machine Learning Advances Materials for Separations, Adsorption, and Catalysis -- Agenparl

Metal-organic frameworks (MOFs) are a class of porous and crystalline materials that are synthesized from inorganic metal ions or clusters connected to organic ligands. Shown are two such materials, HKUST-1 and MIL-100(Fe). An artificial intelligence technique -- machine learning -- is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing. Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability.