Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations

We present a combination of the metadynamics method for the investigation of pressure-induced phase transitions in solids with a neural network representation of high-dimensional density-functional theory (DFT) potential-energy surfaces. In a recent illustration of the method for the complex high-pressure phase diagram of silicon [Behler et al., Phys. Rev. Lett. 100, 185501 (2008)] we have shown that the full sequence of phases can be reconstructed by a series of subsequent simulations. In the present paper we give a detailed account of the underlying methodology and discuss the scope and limitations of the approach, which promises to be a valuable tool for die investigation of a variety of inorganic materials. The method is several orders of magnitude faster than a direct coupling of metadynamics with electronic structure calculations, white the accuracy is essentially maintained, thus providing access to extended simulations of large systems. (c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim