Machine learning offers new framework for heterogeneous catalyst data analysis

Machine learning (ML) transforms the design of heterogeneous catalysts, traditionally driven by trial and error due to the complex interplay of components. BIFOLD researcher Parastoo Semnani from the ML group of BIFOLD Co-Director Klaus-Robert Müller (TU Berlin) and additional researchers from BASLEARN, BASF SE, and others have introduced a new ML framework in the Journal of Physical Chemistry C.