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| Schematic showing water molecules in the denser water phase (left) and the ice phase. (Courtesy: Tobias Morawietz) |
Topics: Artificial Intelligence, Computer Engineering, Computer Science
Artificial neural networks have been used to simulate interactions between water molecules and provide important clues about the remarkable properties of this live-giving substance. The study has been carried out by physicists in Germany and Austria, who used the networks to perform simulations 100,000 times faster than possible with conventional computers. Their work offers explanations for two key properties of water – its maximum density at 4 °C and its melting temperature – but the technique could be expanded to include other aspects of this ubiquitous substance.
Physicists and chemists have long found water's unusual properties difficult to explain. Its density, for example, peaks at around 4 °C, which means that frozen water floats on liquid water – a property that is vital for aquatic creatures that have to survive in cold climates. Massive computer simulations have shown that hydrogen bonds between water molecules play a key role, but these simulations do not tell the whole story.
One key challenge is understanding the role of van der Waals interactions, which arise from quantum fluctuations in the electrical polarizations of water and other molecules. Van der Waals interactions have traditionally been hard to include in computer simulations, but Tobias Morawietz and colleagues at the Ruhr-Universität Bochum and the University of Vienna have now used artificial neural networks (ANNs) to model them in water. ANNs are computer algorithms that "learn" how to perform a specific task by being fed data related to that task. An ANN could, for example, learn how to recognize an individual's face by being fed photographs of people and being told which images are of the target person.
Physics World: Neural networks provide deep insights into the mysteries of water
Hamish Johnston
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