KIT News 2022 from 02.03.2022

KIT News

KIT researchers demonstrate the application of machine learning in the development of metal-organic framework compounds
The researchers have created a web tool for the automatic synthesis prediction of MOFs (shown is a so-called SURMOF structure with an embedded molecule).
Energy and environmental protection, medicine, information and communication: these and many other areas depend on innovative materials. Data-based synthesis strategies can significantly accelerate the development of novel materials and improve their properties. Researchers at the Karlsruhe Institute of Technology (KIT) have used artificial intelligence to determine synthesis strategies for previously unknown metal-organic framework compounds (MOFs). These highly porous crystalline materials can be tailored for a wide range of applications such as material separation, gas storage, catalysis and sensor technology. 

World's first MOF synthesis database 

In the journal Angewandte Chemie, the researchers now report how machine learning (ML) can be used to streamline MOF development. "In this process, the synthesis conditions of a MOF are predicted directly based on the crystal structure," explains Manuel Tsotsalas from KIT's Institute for Functional Interfaces, which conducted the study together with KIT's Institute for Theoretical Computer Science. The data-driven prediction is possible thanks to the world's first MOF synthesis database. To create this database, the required parameters were extracted from the technical literature with the help of natural language processing algorithms. The trained and optimised ML algorithms based on the database clearly surpassed the prediction performance of human experts even in the initial phase. 

or, 01.03.2022