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Biomolecular simulations with atomistic detail are often required to describe interactions with chemical accuracy for applications such as the calculation of free energies of binding or chemical reactions in enzymes. Force fields are typically used for this task but these rely on extensive parameterisation which in cases can lead to limited accuracy and transferability, for example for ligands with unusual functional groups. These limitations can be overcome with first principles calculations with methods such as density functional theory (DFT) but at a much higher computational cost. The use of electrostatic embedding can significantly reduce this cost by representing a portion of the simulated system in terms of highly localised charge distributions. These classical charge distributions are electrostatically coupled with the quantum system and represent the effect of the environment in which the quantum system is embedded. In this paper we describe and evaluate such an embedding scheme in which the polarisation of the electronic density by the embedding charges occurs self-consistently during the calculation of the density. We have implemented this scheme in a linear-scaling DFT program as our aim is to treat with DFT entire biomolecules (such as proteins) and large portions of the solvent. We test this approach in the calculation of interaction energies of ligands with biomolecules and solvent and investigate under what conditions these can be obtained with the same level of accuracy as when the entire system is described by DFT, for a variety of neutral and charged species. [ABSTRACT FROM AUTHOR]

We present a comparison of methods for treating the electrostatic interactions of finite, isolated systems within periodic boundary conditions (PBCs), within density functional theory (DFT), with particular emphasis on linear-scaling (LS) DFT. Often, PBCs are not physically realistic but are an unavoidable consequence of the choice of basis set and the efficacy of using Fourier transforms to compute the Hartree potential. In such cases the effects of PBCs on the calculations need to be avoided, so that the results obtained represent the open rather than the periodic boundary. The very large systems encountered in LS-DFT make the demands of the supercell approximation for isolated systems more difficult to manage, and we show cases where the open boundary (infinite cell) result cannot be obtained from extrapolation of calculations from periodic cells of increasing size. We discuss, implement, and test three very different approaches for overcoming or circumventing the effects of PBCs: truncation of the Coulomb interaction combined with padding of the simulation cell, approaches based on the minimum image convention, and the explicit use of open boundary conditions (OBCs). We have implemented these approaches in the ONETEP LS-DFT program and applied them to a range of systems, including a polar nanorod and a protein. We compare their accuracy, complexity, and rate of convergence with simulation cell size. We demonstrate that corrective approaches within PBCs can achieve the OBC result more efficiently and accurately than pure OBC approaches. [ABSTRACT FROM AUTHOR]

Linear-scaling methods for density functional theory promise to revolutionize the scope and scale of first-principles quantum mechanical calculations. Crystalline silicon has been the system of choice for exploratory tests of such methods in the literature, yet attempts at quantitative comparisons under linear-scaling conditions with traditional methods or experimental results have not been forthcoming. A detailed study using the ONETEP code is reported here, demonstrating for the first time that plane wave accuracy can be achieved in linear-scaling calculations on periodic systems. [ABSTRACT FROM AUTHOR]

Linear-scaling electronic structure methods are essential for calculations on large systems. Some of these approaches use a systematic basis set, the completeness of which may be tuned with an adjustable parameter similar to the energy cut-off of plane-wave techniques. The search for the electronic ground state in such methods suffers from an ill-conditioning which is related to the kinetic contribution to the total energy and which results in unacceptably slow convergence. We present a general preconditioning scheme to overcome this ill-conditioning and implement it within our own first-principles linear-scaling density functional theory method. The scheme may be applied in either real space or reciprocal space with equal success. The rate of convergence is improved by an order of magnitude and is found to be almost independent of the size of the basis.© 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Recently, variants of implicit solvation models for first principles electronic structure calculations based on a direct solution of the nonhomogeneous Poisson equation in real space have been developed. These implicit solvation models are very elegant from a physical point of view as the solute cavity is defined directly via isosurfaces of the electronic density, and the molecular charge is polarized self-consistently by the reaction field of the dielectric continuum which surrounds the solute. Nevertheless, the implementation of these models is technically complex and requires great care. A certain level of care is required from users of such models as a number of numerical parameters need to be given appropriate values to obtain the most accurate and physically relevant results. Here, we describe in what parts of the solvent model each of these numerical parameters is involved and present a detailed study of how they can affect the calculation, using the solvation model which has been implemented in the ONETEP program for linear-scaling density functional theory (DFT) calculations. As ONETEP is capable of DFT calculations with thousands of atoms, we focus our investigation of the numerical parameters with a case study on protein-ligand complexes of the entire 2602-atom T4 Lysozyme L99/M102Q protein. We examine effects on solvation energies and binding energies, which are critical quantities for computational drug optimization and other types of biomolecular simulations. We propose optimal choices of these parameters suitable for routine 'production' calculations. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We propose all the accessible paths of interconversion between the tautomers of 3-acetyl tetronic and 3-acetyl tetramic acids by performing calculations with the density functional B3LYP method and the ab initio MP2 method. Our findings clarify at the atomic level the mechanisms of the equilibria between these tautomers, a topic so far only partially understood on the basis of studies by nuclear magnetic resonance (NMR) spectroscopy. We show that thermal effects via relative Gibbs free energies ΔG must be taken into account in order to reach good quantitative agreement with the available experimental information on the ratios of the most stable tautomers. The calculated 1H and 13C chemical shifts are in agreement with the experimental values from NMR spectroscopy. [Copyright &y& Elsevier]

Linear scaling methods for density-functional theory (DFT) simulations are formulated in terms of localized orbitals in real space, rather than the delocalized eigenstates of conventional approaches. In local-orbital methods, relative to conventional DFT, desirable properties can be lost to some extent, such as the translational invariance of the total energy of a system with respect to small displacements and the smoothness of the potential-energy surface. This has repercussions for calculating accurate ionic forces and geometries. In this work we present results from onetep, our linear scaling method based on localized orbitals in real space. The use of psinc functions for the underlying basis set and on-the-fly optimization of the localized orbitals results in smooth potential-energy surfaces that are consistent with ionic forces calculated using the Hellmann-Feynman theorem. This enables accurate geometry optimization to be performed. Results for surface reconstructions in silicon are presented, along with three example systems demonstrating the performance of a quasi- Newton geometry optimization algorithm: an organic zwitterion, a point defect in an ionic crystal, and a semiconductor nanostructure. [ABSTRACT FROM AUTHOR]