Deep learning models more precisely than ever before how ice crystals form in the atmosphere

Ecosystems are incredibly sensitive, which means that when something is thrown off course the consequences can be dire. The potential to significantly increase the accuracy of weather and climate forecasting. Researchers have used deep learning to model more precisely than ever before how ice crystals form in the atmosphere. These facilitate the aligning of water molecules, which promotes freezing. Once ice crystals have formed, plants are burdened with a myriad of growth implications and tissue damage.

Researchers collect data on ice formation under different laboratory conditions, and that data is fed into weather prediction models under similar real-world conditions. The deep learning techniques are trained on small-scale simulations of 64 water molecules to help them predict how electrons in atoms interact. The models then replicate those interactions on a larger scale, with more atoms and molecules. The process of ice nucleation occurs when small ice crystal embryos form on membrane proteins that act as nucleation sites.

Deep learning breaking the ice on ice formation:

The researchers used deep learning to predict how atoms and molecules behave. The properties of matter emerge from how electrons behave. If researchers could model ice nucleation more accurately, it could give a big boost to weather prediction overall. It could also aid climate forecasting by improving its ability to model clouds, which affect the planet's temperature in complex ways.

This method has been used to model something as complex as the formation of ice crystals, also known as ice nucleation. Ice nucleation in clouds occurs via two primary pathways: homogeneous freezing of liquid particles below about -36 °C and heterogeneous ice nucleation, triggered by ice nuclei that possess surface properties favorable to lowering the energy barrier to crystallization. It's this ability to precisely simulate electron interactions that allowed the team to accurately predict physical and chemical behavior.

The nucleation of ice is vital in cloud physics and impacts a broad range of matters, from the cryopreservation of food, tissues, organs, and stem cells to the prevention of icing on aircraft wings, bridge cables, wind turbines, and other structures. Ice nucleation is currently predicted on the basis of laboratory experiments. It is caused when plants are forced to endure severe temperatures.

This method works well enough sometimes, but often it ends up being inaccurate because of the sheer number of variables involved in actual weather conditions. For farmers and agricultural producers, ice nucleation can be devastating to crops. It's a development that could significantly increase the accuracy of weather and climate forecasting. Thanks to deep learning techniques, it is now possible to use electron interactions to model larger systems over longer periods of time.