The resulting simulation describes how water molecules transition into solid ice with quantum accuracy. This level of accuracy, once thought unreachable due to the amount of computing power it would require, became possible when the researchers incorporated deep neural networks, a form of artificial intelligence, into their methods. The study was published in the journal Proceedings of the National Academy of Sciences.
“In a sense, this is like a dream come true,” said Roberto Car, Princeton’s Ralph W. *31 Dornte Professor in Chemistry, who co-pioneered the approach of simulating molecular behaviors based on the underlying quantum laws more than 35 years ago. “Our hope then was that eventually we would be able to study systems like this one, but it was not possible without further conceptual development, and that development came via a completely different field, that of artificial intelligence and data science.”
The ability to model the initial steps in freezing water, a process called ice nucleation, could improve accuracy of weather and climate modeling as well as other processes like flash-freezing food.
The new approach enables the researchers to track the activity of hundreds of thousands of atoms over time periods that are thousands of times longer, albeit still just fractions of a second, than in early studies.
Car co-invented the approach to using underlying quantum mechanical laws to predict the physical movements of atoms and molecules. Quantum mechanical laws dictate how atoms bind to each other to form molecules, and how molecules join with each other to form everyday objects.













