What Can DP Do too? | Science Sub-journal, Understanding More Realistic Solutions, ML Force Field Speeds Up by Six Orders of Magnitude, More Efficiently Representing the Spatiotemporal Relationship of Water Molecules
Water is not only one of the most familiar substances to humans but also a central figure in the long history of physical chemistry. The tetrahedral arrangement and network interactions between its molecules distinguish it from simple liquids.
For a long time, there has been no specific conclusion regarding whether there is a liquid - liquid critical point (LLCP) in water. Besides, researchers' understanding of water, especially when it acts as a solvent, is still incomplete.
To address the problem of technically and reasonably representing the thermodynamic and kinetic properties of water after the introduction of other chemical substances, a team from Seoul National University in South Korea proposed a scheme to examine the spatiotemporal characterization of water using a machine learning force field (MLFF) through deep potential molecular dynamics (DPMD).
This research, titled "Spatiotemporal characterization of water diffusion anomalies in saline solutions using machine learning force field", was published in "Science Advances" on December 11, 2024.
Currently, most water models are unable to fully capture the dynamic behavior of water after the addition of salt. Although classical force fields provide important insights, their simplifications and the omission of dynamic charge effects may distort our understanding of the real behavior of water.
The application advantages of MLFF in fields such as materials science and its processing speed, which is more than six orders of magnitude faster than first - principles methods in systems composed of several hundred atoms, make it stand out among all the options.