Multiscale modelling of soft matter is an emerging field that has made rapid progress in the past decade.Several methods for systematic coarse-graining of molecular liquids and soft matter systems have been proposed in recent years. Herein, we review these methods and discuss a selected number of applications as well as limitations of the models and remaining challenges in developing representative and transferable pair potentials.

How are water’s material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth’s living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies—water’s solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions—hydroxide and protons—diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water’s molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water’s orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties.

Systematically coarse grained models for complex fluids usually lack chemical and thermodynamic transferability. Efforts to improve transferability require the development of effective potentials with unequivocal physical significance. In this paper, we introduce conditional reversible work (CRW) potentials that describe nonbonded interactions in coarse grained models at the pair level. The method used to obtain these potentials is straightforward to implement, can be readily extended to compute hydration contributions in implicit-solvent potentials, and is easy to automize. As a first illustration of the method, we present CRW potentials for 3-site models of hexane and toluene. The temperature-transferability of the liquid phase density obtained with these potentials has been investigated, and a comparison has been made with effective potentials obtained by the iterative Boltzmann inversion method.

The representability and transferability of effective pair potentials used in multiscale simulations of soft matter systems is ill understood. In this paper, we study liquid state systems composed of n-alkanes, the coarse-grained (CG) potential of which may be assumed pairwise additive and has been obtained using the conditional reversible work (CRW) method. The CRW method is a free-energy-based coarse-graining procedure, which, by means of performing the coarse graining at pair level, rigorously provides a pair potential that describes the interaction free energy between two mapped atom groups (beads) embedded in their respective chemical environments. The pairwise nature of the interactions combined with their dependence on the chemically bonded environment makes CRW potentials ideally suited in studies of chemical transferability. We report CRW potentials for hexane using a mapping scheme that merges two heavy atoms in one CG bead. It is shown that the model is chemically and thermodynamically transferable to alkanes of different chain lengths in the liquid phase at temperatures between the melting and the boiling point under atmospheric (1 atm) pressure conditions. It is further shown that CRW-CG potentials may be readily obtained from a single simulation of the liquid state using the free energy perturbation method, thereby providing a fast and versatile molecular coarse graining method for aliphatic molecules.

Protein structures can be predicted by combining atomistic simulations and coarse information.