Using machine learning to better understand how water behaves
The 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 could separate into two liquid phases of different densities. Like oil and water, these phases don’t mix and may help explain some of the other strange behavior of water, like the way it becomes less dense as it gets colder.
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 the phase changes of water, opening up more avenues for a better theoretical understanding of various substances. With this technique, the researchers found strong computational evidence supporting the liquid-liquid transition of water that can be applied to real-world systems that use water to operate.
“We’re doing this with very detailed quantum chemistry calculations that try to get as close as possible to actual physics and the physical chemistry of water,” said Thomas Gartner, an assistant professor in Georgia Tech’s School of Chemical and Biomolecular Engineering. It’s the first time anyone has been able to study this transition with this level of precision.”
The research was presented in the article, “Liquid-liquid transition in water from first principles,” In the diary Physical Review Letterswith coauthors from Princeton University.
To better understand how water interacts, the researchers ran 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 individual molecules and see how they move and interact in real time,” he said. “This is what we’re doing by creating almost a computational movie.”
The researchers analyzed how the molecules move and characterized the structure of the liquid at different water temperatures and pressures, mimicking the phase separation between high-density and low-density liquids. They collected a large amount of data (they ran some simulations for up to a year) and continued to fine-tune their algorithms to get more accurate results.
Even a decade ago, running such long and detailed simulations would not have been possible, but today’s machine learning offers 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 computation significantly faster than traditional techniques, allowing the simulations to progress much more efficiently.
Machine learning isn’t 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, then you have validated your accuracy.
“One of the challenges of 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 envelope here, so that’s another reason why it’s so important that we try to do this using multiple 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 aquatic environments in the solar system, from the oceans of Europa to the water at the center of comets. However, these findings could also help researchers better explain and predict the strange and complex physical chemistry of water, informing water use in industrial processesdevelop better climate models, and more.
The work is even more generalizable, according to Gartner. Water is a well-studied area of research, but this methodology could be expanded to other difficult-to-simulate materials such as polymers or complex phenomena such as chemical reactions.
“Water is so fundamental to life and industry, so this particular question of whether water can go through this transition phase has been a long-standing problem, and if we can move towards an answer, that’s important.” , said. “But now we have this really powerful new computational technique, but we don’t know what the limits are yet, and there’s a lot of room to advance the field.”
Thomas E. Gartner et al, Liquid-Liquid Transition in Water from First Principles, Physical Review Letters (2022). DOI: 10.1103/PhysRevLett.129.255702
Georgia Institute of Technology
Citation: Using machine learning to better understand how water behaves (Dec 17, 2022) Retrieved Dec 17, 2022 from https://phys.org/news/2022-12-machine.html
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