Artificial intelligence researchers have celebrated a string of successes with neural networks, computer programs that roughly mimic how our brains are organized. But despite rapid progress, neural networks remain relatively inflexible, with little ability to change on the fly or adjust to unfamiliar circumstances.
In a new study, published in Proceedings of the National Academy of Sciences, researchers from Los Alamos National Laboratory have proposed incorporating more of the mathematics of quantum mechanics into the structure of the machine learning predictions. Using the specific positions of atoms within a molecule, the machine learning model predicts an effective Hamiltonian matrix, which describes the various possible electronic states along with their associated energies.
