Machine learning speeds up atomistic simulations of water and ice

Why is water densest at around 4 degrees Celsius? Why does ice float? Why does heavy water have a different melting point compared to normal water? Why do snowflakes have a six-fold symmetry? A collaborative study of researchers from the École Polytechnique Fédérale de Lausanne, the University of Göttingen and the University of Vienna and just published in the Proceedings of the National Academy of Sciences of the USA, provides physical insights into these questions by marrying data-driven machine learning techniques and quantum mechanics.

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