A first principles based polarizable O(N) interatomic force field for bulk silica

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Interactions at the Peptide/Silicon Surfaces: Evidence of Peptide Multilayer Assembly

Selective deposition of peptides from liquid solutions to n- and p-doped silicon has been demonstrated. The selectivity is governed by peptide/silicon adhesion differences. A noninvasive, fast characterization of the obtained peptide layers is required to promote their application for interfacing silicon-based devices with biological material. In this study we show that spectroscopic ellipsometry-a method increasingly used for the investigation of biointerfaces-can provide essential information about the amount of adsorbed peptide material and the degree of coverage on silicon surfaces. We observed the formation of peptide multilayers for a strongly binding adhesion peptide on p-doped silicon. Application of the patterned layer ellipsometric evaluation method combined with Sellmeier dispersion led to physically consistent results, which describe well the optical properties of peptide layers in the visible spectral range. This evaluation allowed the estimation of surface coverage, which is an important indicator of adsorption quality. The ellipsometric findings were well supported by atomic force microscopy results.

Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces

We present a molecular dynamics scheme which combines first-principles and machine-learning (ML) techniques in a single information-efficient approach. Forces on atoms are either predicted by Bayesian inference or, if necessary, computed by on-the-fly quantum-mechanical (QM) calculations and added to a growing ML database, whose completeness is, thus, never required. As a result, the scheme is accurate and general, while progressively fewer QM calls are needed when a new chemical process is encountered for the second and subsequent times, as demonstrated by tests on crystalline and molten silicon.

Influence of polarizability on metal oxide properties studied by molecular dynamics simulations

We have studied the dependence of metal oxide properties in molecular dynamics (MD) simulations on the polarizability of oxygen ions. We present studies of both liquid and crystalline structures of silica (SiO(2)), magnesia (MgO) and alumina (Al(2)O(3)). For each of the three oxides, two separately optimized sets of force fields were used: (i) long-range Coulomb interactions between oxide and metal ions combined with a short-range pair potential; (ii) extension of force field (i) by adding polarizability to the oxygen ions. We show that while an effective potential of type (i) without polarizable oxygen ions can describe radial distributions and lattice constants reasonably well, potentials of type (ii) are required to obtain correct values for bond angles and the equation of state. The importance of polarizability for metal oxide properties decreases with increasing temperature.