Using machine learning to better understand how water behaves

Water has puzzled scientists for decades. For the last 30 years or so, they have theorized that when cooled to a very low temperature like -100C, water might separate into two liquid phases of different densities. Like oil and water, these phases don’t mix and may help explain some of water’s other strange behavior, such as how it becomes less dense as it cools.

However, it is almost impossible to study this phenomenon in a laboratory because water crystallizes into ice so quickly at such low temperatures. Now, new research from the Georgia Institute of Technology uses machine learning models to better understand water’s phase changes, opening more avenues for a better theoretical understanding of various substances. Using this technique, the researchers found strong computational evidence in support of water’s liquid-liquid transition that can be applied to real systems that use water to operate.

“We do this with very detailed quantum chemistry calculations that try to be as close as possible to the actual physics and physical chemistry of water,” said Thomas Gartner, an assistant professor in the School of Chemical and Biomolecular Engineering at Georgia Tech . “This is the first time anyone has been able to study this transition with this level of precision.”

The research was presented in the paper, “Liquid-Liquid Transition in Water From First Principles,” in the journal Physical Review Letterswith co-authors from Princeton University.

Simulate water

To better understand how water interacts, the researchers performed molecular simulations on supercomputers, which Gartner compared to a virtual microscope.

“If you had an infinitely powerful microscope, you could zoom down to the level of the individual molecules and watch them move and interact in real time,” he said. “That’s what we’re doing by almost creating a computer film.”

The researchers analyzed how the molecules move and characterized the liquid structure at different water temperatures and pressures, which mimic the phase separation between the high- and low-density liquids. They collected extensive data—with some simulations lasting up to a year—and continued to refine their algorithms for more accurate results.

Even a decade ago, such long and detailed simulations would not have been possible, but today machine learning has provided a shortcut. The researchers used a machine learning algorithm that calculated the energy of how water molecules interact with each other. This model performed the calculation significantly faster than traditional techniques, making the simulations progress much more efficiently.

Machine learning is not perfect, so these long simulations also improved the accuracy of the predictions. The researchers were careful to test their predictions with different types of simulation algorithms. If multiple simulations gave similar results, this validated their accuracy.

“One of the challenges with this work is that there isn’t a lot of data that we can compare to, because it’s a problem that’s almost impossible to study experimentally,” Gartner said. “We’re really pushing the boundaries here, so that’s another reason why it’s so important that we try to do this using several different computational techniques.”

Beyond the water

Some of the conditions the researchers tested were extremes that probably don’t exist directly on Earth, but could potentially be present in various water environments of the solar system, from the oceans of Europa to water in the center of comets. Yet these findings could also help researchers better explain and predict water’s strange and complex physical chemistry, inform water’s use in industrial processes, develop better climate models, and more.

The work is even more generalizable, according to Gartner. Water is a well-studied research area, but this methodology can be extended to other difficult-to-simulate materials such as polymers, or complex phenomena such as chemical reactions.

“Water is so central to life and industry, so this particular question of whether water can undergo this phase transition has been a long-standing problem, and if we can move toward an answer, that’s important,” he said. “But now we have this very powerful new computational technique, but we don’t know what the limits are yet and there’s a lot of room to move the field forward.”