A local Bayesian optimizer for atomic structures

A local optimization method based on Bayesian Gaussian Processes is developed and applied to atomic structures. The method is applied to a variety of systems including molecules, clusters, bulk materials, and molecules at surfaces. The approach is seen to compare favorably to standard optimization algorithms like conjugate gradient or BFGS in all cases. The method relies on prediction of surrogate potential energy surfaces, which are fast to optimize, and which are gradually improved as the calculation proceeds. The method includes a few hyperparameters, the optimization of which may lead to further improvements of the computational speed.Comment: 10 pages, 5 figure

Localized atomic basis set in the projector augmented wave method

We present an implementation of localized atomic orbital basis sets in the projector augmented wave (PAW) formalism within the density functional theory (DFT). The implementation in the real-space GPAW code provides a complementary basis set to the accurate but computationally more demanding grid representation. The possibility to switch seamlessly between the two representations implies that simulations employing the local basis can be fine tuned at the end of the calculation by switching to the grid, thereby combining the strength of the two representations for optimal performance. The implementation is tested by calculating atomization energies and equilibrium bulk properties of a variety of molecules and solids, comparing to the grid results. Finally, it is demonstrated how a grid-quality structure optimization can be performed with significantly reduced computational effort by switching between the grid and basis representations.Comment: 10 pages, 5 figures. http://prb.aps.org.globalproxy.cvt.dk/abstract/PRB/v80/i19/e19511