Abstract
Existing computers can calculate the exact properties of only the smallest molecules thanks to the mathematical complexity of quantum mechanics. So chemists have invented methods, including force fields, density functional theory (DFT), and the coupled-cluster singles, doubles, and triples technique (CCSD(T)), to approximate values like molecular energies and forces. Users can get quick answers or accurate ones with these methods. For example, CCSD(T) is accurate but slow, compared with force fields. Some researchers think machine learning could offer a better way. At the ACS meeting last week, Adrian Roitberg of the University of Florida described a method that can achieve the accuracy of CCSD(T) in the computational time of force fields. He calls it Accurate NeurAl networK engINe for Molecular Energies (ANAKIN-ME). Roitberg said the third version of the method, which his team calls ANI-1ccx, can predict the forces and energy of a molecule with only the positions of its
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