Recent research from U-M ChE professors Suljo Linic and Bryan Goldsmith and their co-advised PhD student Jacques Esterhuizen explores recent advances in machine learning approaches for heterogeneous catalysis.
Suljo Linic
Interpretable machine learning in catalysis
War and Professorship
U-M College of Engineering honors Suljo Linic on his amazing journey to the Martin Lewis Perl Collegiate Professor of Chemical Engineering.
Machine learning links material composition and performance in catalysts
Understanding how to design better catalysts could enable sustainable energy tech and make everyday chemicals more environmentally friendly.
Solving the plastic shortage
New catalyst could stabilize supplies of one of the world’s most important plastics.
Chemistry and energy: Machine learning to understand catalyst interactions
Toward harnessing machine learning to design the materials we want.
Harnessing light to drive chemical reactions
The mechanism transferring light energy from capturer to catalyst is explained, paving the way to design better reactions that use less energy and produce less waste.
