Projective simulation with generalization.

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

A self-interaction-free local hybrid functional: accurate binding energies vis-à-vis accurate ionization potentials from Kohn-Sham eigenvalues.

We present and test a new approximation for the exchange-correlation (xc) energy of Kohn-Sham density functional theory. It combines exact exchange with a compatible non-local correlation functional. The functional is by construction free of one-electron self-interaction, respects constraints derived from uniform coordinate scaling, and has the correct asymptotic behavior of the xc energy density. It contains one parameter that is not determined ab initio. We investigate whether it is possible to construct a functional that yields accurate binding energies and affords other advantages, specifically Kohn-Sham eigenvalues that reliably reflect ionization potentials. Tests for a set of atoms and small molecules show that within our local-hybrid form accurate binding energies can be achieved by proper optimization of the free parameter in our functional, along with an improvement in dissociation energy curves and in Kohn-Sham eigenvalues. However, the correspondence of the latter to experimental ionization potentials is not yet satisfactory, and if we choose to optimize their prediction, a rather different value of the functional's parameter is obtained. We put this finding in a larger context by discussing similar observations for other functionals and possible directions for further functional development that our findings suggest.

Fully Numerical All-Electron Solutions of the Optimized Effective Potential Equation for Diatomic Molecules.

We present an approach for fully numerical, all-electron solutions of the optimized effective potential equation within Kohn-Sham density functional theory for diatomic molecules. The approach is based on a real-space, prolate-spheroidal coordinate grid for solving the all-electron Kohn-Sham equations and an iterative scheme for solving the optimized effective potential equation. The accuracy of this method is demonstrated by comparison with previously reported calculations. New fully numerical benchmark results for selected diatomic molecules are provided.

Size-dependent spintronic properties of dilute magnetic semiconductor nanocrystals.

The electronic structure and magnetic properties of Mn-doped Ge, GaAs, and ZnSe nanocrystals are investigated using real space ab initio pseudopotentials constructed within the local spin-density approximation. The ferromagnetic and half-metallicity trends found in the bulk are preserved in the nanocrystals. However, the Mn-related impurity states become much deeper in energy with decreasing nanocrystalline size, causing the ferromagnetic stabilization to be dominated by double exchange via localized holes rather than by a Zener-like mechanism.