Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

A team of U of T Engineering researchers is leveraging machine learning to enhance the manufacturing of common everyday items. They have created an algorithm that efficiently sifts through thousands of possible geometric configurations to design better industrial catalysts.

“Catalysts speed up chemical reactions, making manufacturing more efficient and less costly,” says Professor Chandra Veer Singh (MSE). “They are used to produce everything from plastics to industrial fertilizers, and researchers around the world are constantly trying to improve their performance. Alloying is a key strategy in this field.” U of T Engineering researchers use machine learning to design smarter industrial catalysts. By Tyler Irving U of T Engineering news.

 

Link to research article; Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

 


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