Machine learning cracks quantum chemistry conundrum

A new machine learning tool can calculate the energy required to make--or break--a molecule with higher accuracy than conventional methods. While the tool can currently only handle simple molecules, it paves the way for future insights in quantum chemistry. "Using machine learning to solve the fundamental equations governing quantum chemistry has been an open problem for several years, and there's a lot of excitement around it right now," says co-creator Giuseppe Carleo, a research scientist at the Flatiron Institute's Center for Computational Quantum Physics in New York City. A better understanding of the formation and destruction of molecules, he says, could reveal the inner workings of the chemical reactions vital to life. The team's tool estimates the amount of energy needed to assemble or pull apart a molecule, such as water or ammonia.