Water is perceived to be one of the simplest substances in the world, modeling its behavior on the atomic or molecular level has frustrated scientists for decades.
A new study from the U.S. Department of Energy’s (DOE) Argonne National Laboratory has achieved a breakthrough in the effort to mathematically represent how water behaves.
To do so, Argonne researchers used machine learning to develop a new, computationally inexpensive water model that more accurately represents the thermodynamic properties of water, including how water changes to ice at the molecular scale.
In the study, researchers used a machine learning workflow to optimize a new molecular model of water. They trained their model against extensive experimental data to generate a highly accurate molecular-scale model of water’s properties.
“We are trying to understand how to navigate the complex parameter space for any given model in order to capture a wide spectrum of water’s properties, which is extremely difficult,” Sankaranarayanan explained. “
For the researchers, the choice to use entire water molecules as the fundamental unit in the model allowed them to perform the simulation at low computational cost.
“While traditionally these simple models introduce a number of approximations and often suffer from poor accuracy, machine learning allows us to create a much more accurate model while maintaining simplicity,” said University of Louisville assistant professor Badri Narayanan, a co-first author of the study.
However, even with this reduced computational expense, some physical properties can be difficult to simulate without large-scale supercomputers.
To achieve the high accuracy of the coarse-grained model, the researchers trained the model using information drawn from nearly a billion atomic-scale configurations involving temperature-dependent properties that are well known.
“Essentially, we said to our model, ‘look, this is what the properties are,’ and asked it to give us parameters that were able to reproduce them,” Chan said.
The researchers also showed that their approach could be used to improve the performance of other existing atomistic and molecular models. “We were able to significantly improve the performance of existing high-quality water models using our hierarchical approach.
In principle, we should be able to revisit all molecular models and help each one of them attain their best performance,” Sankaranarayanan said.