arXiv:0910.1019physics.comp-ph
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
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Abstract
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, derived from quantum mechanical calculations. The resulting model does not have a fixed functional form and hence is capable of modeling complex potential energy landscapes. It is systematically improvable with more data. We apply the method to bulk carbon, silicon and germanium and test it by calculating properties of the crystals at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.
People have tried applying machine learning to learning many-body potentials in physics for use in speeding up quantum molecular dynamics while maintaining most of the accuracy. What you'd do is say that the total energy of the system is a sum of local potentials on each atom R…
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