Using machine learning to help generalize automated chemistry

Researchers combined machine learning and a molecule-making machine to find the best conditions for automated complex chemistry. Pictured, from left: University of Illinois chemistry professor Martin D. Burke, materials science and engineering professor Charles M. Schroeder, graduate student Nicholas Angello and postdoctoral researcher Vandana Rathore. Pictured on the screen behind them are international collaborators, led by professors Bartosz A. Grzybowski and Alán Aspuru-Guzik. Artificial intelligence, "building-block" chemistry and a molecule-making machine were combined to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials research – a finding that could speed innovation and drug discovery as well as make complex chemistry automated and accessible. With the machine-generated optimized conditions, researchers at the University of Illinois Urbana-Champaign and collaborators in Poland and Canada doubled the average yield of a special, hard-to-optimize type of reaction linking carbon atoms together in pharmaceutically important molecules.