Machine Learning for Quantum Chemistry

Quantum chemistry, the study of chemical properties and processes at the quantum scale, has a big drawback: Accurate calculations are resource-intensive and time consuming, with routine chemical studies involving computations that take days or longer.

Using a new quantum chemistry tool, OrbNet, 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 has been developed at Caltech.

OrbNet uses a graph neural network, a type of machine-learning system that represents information as “nodes,” which contain data, and “edges,” which represent the ways those chunks of data are related to one another.

Like all machine-learning systems, OrbNet needs to be trained to perform an assigned task, similar to how a person who gets a new job needs to be trained for it. OrbNet learned to predict molecular properties on the basis of accurate reference quantum mechanical calculations. Currently, OrbNet has been trained on approximately 100,000 molecules. (

The paper has been published in the Journal of Chemical Physics.

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