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An international team of scientists including Kieron Burke, UCI professor of chemistry, has created a machine-learning algorithm that predicts molecular behavior. The breakthrough may aid in the development of pharmaceuticals and materials to enhance the performance of batteries, solar cells and digital displays. In a study published in Nature Communications, the researchers describe how their algorithm gathers knowledge about atomic interactions in a molecule and then uses that information to anticipate new actions. Complex atomic interactions are prescribed by quantum mechanical calculations. The research team, which also included scientists from New York University and the Technical University of Berlin, found a way to simulate chemical behavior within a molecule without having to perform quantum-level number crunching.