Grand challenges in quantum-classical modeling of molecule-surface interactions.

A detailed understanding of the adsorption of small molecules or macromolecules to a materials surface is of importance, for example, in the context of material and biomaterial research. Classical atomistic simulations in principle provide microscopic insight in the complex entropic and enthalpic interplay at the interface. However, an application of classical atomistic simulation techniques to such interface systems is a nontrivial problem, mostly because commonly used force fields cannot be straightforwardly applied, as they are usually developed to reproduce bulk properties of either solids or liquids but not the interfacial region between two phases. Therefore, a dual-scale modeling approach has often been the method of choice in the past, in which the classical force field is parameterized such that quantum chemical information on near-surface conformations and adsorption energies is reproduced by the classical force field. We will discuss in this review the current state-of-the-art of quantum-classical modeling of molecule-surface interactions and outline the major challenges in this field. In this context, we will, among other things, lay emphasis on discussing ways to obtain representable force fields and propose systematic and system-independent strategies to optimize the quantum-classical fitting procedure.

Thermodynamic transferability of coarse-grained potentials for polymer-additive systems.

In this work we study the transferability of systematically coarse-grained (CG) potentials for polymer-additive systems. The CG nonbonded potentials between the polymer (atactic polystyrene) and three different additives (ethylbenzene, methane and neopentane) are derived using the Conditional Reversible Work (CRW) method, recently proposed by us [Brini et al., Phys. Chem. Chem. Phys., 2011, 13, 10468-10474]. A CRW-based effective pair potential corresponds to the interaction free energy between the two atom groups of an atomistic parent model that represent the coarse-grained interaction sites. Since the CRW coarse-graining procedure does not involve any form of parameterisation, thermodynamic and structural properties of the condensed phase are predictions of the model. We show in this work that CRW-based CG models of polymer-additive systems are capable of predicting the correct structural correlations in the mixture. Furthermore, the excess chemical potentials of the additives obtained with the CRW-based CG models and the united-atom parent models are in satisfactory agreement and the CRW-based CG models show a good temperature transferability. The temperature transferability of the model is discussed by analysing the entropic and enthalpic contributions to the excess chemical potentials. We find that CRW-based CG models provide good predictions of the excess entropies, while discrepancies are observed in the excess enthalpies. Overall, we show that the CRW CG potentials are suitable to model structural and thermodynamic properties of polymer-penetrant systems.

Modelling molecule-surface interactions--an automated quantum-classical approach using a genetic algorithm.

We present an automated and efficient method to develop force fields for molecule-surface interactions. A genetic algorithm (GA) is used to parameterise a classical force field so that the classical adsorption energy landscape of a molecule on a surface matches the corresponding landscape from density functional theory (DFT) calculations. The procedure performs a sophisticated search in the parameter phase space and converges very quickly. The method is capable of fitting a significant number of structures and corresponding adsorption energies. Water on a ZnO(0001) surface was chosen as a benchmark system but the method is implemented in a flexible way and can be applied to any system of interest. In the present case, pairwise Lennard Jones (LJ) and Coulomb potentials are used to describe the molecule-surface interactions. In the course of the fitting procedure, the LJ parameters are refined in order to reproduce the adsorption energy landscape. The classical model is capable of describing a wide range of energies, which is essential for a realistic description of a fluid-solid interface.

2H and 13C NMR studies on the temperature-dependent water and protein dynamics in hydrated elastin, myoglobin and collagen.

(2)H NMR spin-lattice relaxation and line-shape analyses are performed to study the temperature-dependent dynamics of water in the hydration shells of myoglobin, elastin, and collagen. The results show that the dynamical behaviors of the hydration waters are similar for these proteins when using comparable hydration levels of h=0.25-0.43. Since water dynamics is characterized by strongly nonexponential correlation functions, we use a Cole-Cole spectral density for spin-lattice relaxation analysis, leading to correlation times, which are in nice agreement with results for the main dielectric relaxation process observed for various proteins in the literature. The temperature dependence can roughly be described by an Arrhenius law, with the possibility of a weak crossover in the vicinity of 220 K. Near ambient temperatures, the results substantially depend on the exact shape of the spectral density so that deviations from an Arrhenius behavior cannot be excluded in the high-temperature regime. However, for the studied proteins, the data give no evidence for the existence of a sharp fragile-to-strong transition reported for lysozyme at about 220 K. Line-shape analysis reveals that the mechanism for the rotational motion of hydration waters changes in the vicinity of 220 K. For myoglobin, we observe an isotropic motion at high temperatures and an anisotropic large-amplitude motion at low temperatures. Both mechanisms coexist in the vicinity of 220 K. (13)C CP MAS spectra show that hydration results in enhanced elastin dynamics at ambient temperatures, where the enhancement varies among different amino acids. Upon cooling, the enhanced mobility decreases. Comparison of (2)H and (13)C NMR data reveals that the observed protein dynamics is slower than the water dynamics.