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Close up of the semi-automated synthesis robot used to generate training data Photo Credit: ICReDD |
Researchers used a chemical synthesis robot and computationally cost effective A.I. model to successfully predict and validate highly selective catalysts.
Artificial intelligence (A.I.) has made headlines recently with the advent of ChatGPT’s language processing capabilities. Creating a similarly powerful tool for chemical reaction design remains a significant challenge, especially for complex catalytic reactions. To help address this challenge, researchers at the Institute for Chemical Reaction Design and Discovery and the Max Planck Institut für Kohlenforschung have demonstrated a machine learning method that utilizes advanced yet efficient 2D chemical descriptors to accurately predict highly selective asymmetric catalysts—without the need for quantum chemical computations.
“There have been several advanced technologies which can “predict” catalyst structures, but those methods often required large investments of calculation resources and time; yet their accuracy was still limited,” said joint first author Nobuya Tsuji. “In this project, we have developed a predictive model which you can run even with an everyday laptop PC.”