Neural network potential-energy surfaces for atomistic simulations

Studying chemical reactions in computer simulations requires a reliable description of the atomic interactions. While for systems of moderate size precise electronic structure calculations can be carried out to determine the energy and the forces, for large systems it is necessary to employ more efficient potentials. In past decades a huge number of such potentials has been developed for a variety of systems. Still, for the investigation of many chemical problems the accuracy of the available potentials is not yet satisfactory. In particular, chemical reactions at surfaces, strongly varying bonding patterns in materials science, and the complex reactivity of metal centers in coordination chemistry are prominent examples where most existing potentials are not sufficiently accurate. In recent years, a new class of interatomic potentials based on artificial neural networks has emerged. These potentials have a very flexible functional form and can therefore accurately adapt to a reference set of electronic structure energies. To date, neural network potentials have been constructed for a number of systems. They are promising candidates for future applications in large-scale molecular dynamics simulations, because they can be evaluated several orders of magnitude faster than the underlying electronic structure energies. However, further methodical developments are needed to reach this goal. In this review the current status of neural network potentials is summarized. Open problems and limitations of the hitherto proposed methods are discussed, and some possible solutions are presented.