Machine Learning Speeds Up Quantum Chemistry Calculations

Quantum chemistry, the study of chemical properties and processes at the quantum scale, has opened many paths to research and discovery in modern chemistry. Without ever handling a beaker or a test tube, chemists can make predictions about the properties of a given atom or molecule and how it will undergo chemical reactions by studying its electronic structure--how its electrons are arranged in orbitals--and how those electrons interact with those of other compounds or atoms. However, as powerful as quantum chemistry has shown itself to be, it also has a big drawback: Accurate calculations are resource-intensive and time consuming, with routine chemical studies involving computations that take days or longer. Now, thanks to a new quantum chemistry tool that uses machine learning, quantum-chemistry calculations can be performed 1,000 times faster than previously possible, allowing accurate quantum chemistry research to be performed faster than ever before. The tool, called OrbNet, was developed through a partnership between Caltech's Tom Miller, professor of chemistry, and Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences.