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Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. The new approach allows for a degree of precision and efficiency that has never been achieved before.
Molecular dynamics simulations are used in natural and material science to predict the properties and behavior of different materials. In the past, these simulations were usually based on mechanistic models that are unable to integrate important insights from the quantum mechanics. This work now published in Nature Communications substantially improves the prediction capabilities of modern atomistic modeling in chemistry, biology, and the material sciences.
Exact - Knowledge - Dynamics - Substance - Words
Exact knowledge about the molecular dynamics of a substance, in other words precise knowledge of the possible states and interactions of single atoms in a molecule, enables us to not only understand many chemical and physical reactions but also to make use of these. "Machine learning techniques have dramatically altered work in many disciplines, but up until now, little use has been made of them in molecular dynamics simulations," says Klaus-Robert Müller, Professor of machine learning at TU Berlin. The problem: Most standard algorithms have been developed with the understanding that the amount of data to be processed is of no relevance. "This doesn't apply, however, for accurate quantum mechanical calculations of a molecule, where every single data point is crucial and the individual calculation for larger molecules can take several weeks or even months. The enormous computational resources required to do this has meant that precise...
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