Molecular Dynamics Simulations of Aqueous Systems

Molecular dynamics (MD) simulations have become an indispensable tool for probing the behaviour of water and aqueous solutions at the atomic level. By integrating Newton’s equations of motion for each atom, MD provides time-resolved trajectories that reveal solvation structure, hydrogen-bond networks, ion transport, interfacial phenomena and phase behaviour. Advances in force-field development, enhanced-sampling algorithms and machine-learning frameworks have extended accessible length and time scales from picoseconds to microseconds and beyond. Applications span biomolecular recognition, membrane transport, desalination, electrochemistry, environmental remediation and the design of functional materials, offering predictive insights into processes as diverse as protein folding in water, nanoparticle stability, electrolyte effects and energy storage in aqueous media.

Research from Nature Portfolio

Recent studies have introduced end-to-end deep-learning architectures that map raw MD trajectories directly onto kinetically relevant states. By embedding the variational approach for Markov processes into neural networks, these frameworks automatically learn optimal collective coordinates and state definitions, bypassing manual feature engineering. Such advances yield more accurate and interpretable kinetic models for aqueous biomolecular systems, enabling robust characterisation of conformational switching, ligand association and solvent-mediated transport in complex environments.

Molecular Dynamics Simulations of Aqueous Systems publication trend

The graph below shows the total number of articles in molecular dynamics simulations of aqueous systems across all publications each year (not limited to Nature Index journals).

Technical terms

Molecular dynamics simulation: computational technique that calculates the time evolution of a set of atoms by integrating Newton’s equations of motion.

Force field: collection of mathematical functions and parameters that approximate the potential energy of a molecular system.

Coarse-graining: reduction of system complexity by representing groups of atoms as single interaction sites to extend accessible simulation scales.

Hydrogen bond: directional interaction between a hydrogen atom covalently bound to an electronegative atom and a lone pair of another electronegative atom.

Markov state model: statistical framework that partitions a system’s conformational space into discrete states and quantifies the rates of transitions between them.

References

1. The physics behind water irregularity. Physics Reports (2023).
2. Polarizable Water Model for the Coarse-Grained MARTINI Force Field. PLOS Computational Biology (2010).
3. Models and mechanisms of Hofmeister effects in electrolyte solutions, and colloid and protein systems revisited. Chemical Society Reviews (2014).
4. VAMPnets for deep learning of molecular kinetics. Nature Communications (2018).