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Deep learning can almost perfectly predict how ice forms

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Researchers have used deep learning to model more precisely than ever before how ice crystals form in the atmosphere. Their paper, published this week in PNAS, hints at the potential to significantly increase the accuracy of weather and climate forecasting.

The researchers used deep learning to predict how atoms and molecules behave. First, models were trained on small-scale simulations of 64 water molecules to help them predict how electrons in atoms interact. The models then replicated those interactions on a larger scale, with more atoms and molecules. It’s this ability to precisely simulate electron interactions that allowed the team to accurately predict physical and chemical behavior.

“The properties of matter emerge from how electrons behave,” says Pablo Piaggi, a research fellow at Princeton University and the lead author on the study. “Simulating explicitly what happens at that level is a way to capture much more rich physical phenomena.”