MatterSim: A deep-learning model for materials under real-world conditions

The image features a complex network of interconnected nodes with a molecular structure, illuminated in blue against a dark background.

In the quest for groundbreaking materials crucial to nanoelectronics, energy storage, and healthcare, a critical challenge looms: predicting a material’s properties before it is even created. This is no small feat, with any combination of 118 elements in the periodic table, and the range of temperatures and pressures under which materials are synthesized and operated. These factors drastically affect atomic interactions within materials, making accurate property prediction and behavior simulation exceedingly demanding.

Here at Microsoft



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