From chemical Graphs To Structures

The machine learning model Graph2Structure uses graphs of chemical compounds ( left ) to predict their 3D coordinates ( right ). Dominik Lemm, University of Vienna) - Three-dimensional (3D) configurations of atoms dictate all materials properties. Quantitative predictions of accurate equilibrium structures, 3D coordinates of all atoms, from a chemical graph, a representation of the structural formula, is a challenging and computationally expensive task which is at the beginning of practically every computational chemistry workflow. Researchers at the University of Vienna have now developed a new machine learning based model to shortcut expensive calculations to directly predict structures from graphs. The new method for "Machine learning based energy-free structure predictions of molecules, transition states, and solids" is presented Communications. Albeit commonly depicted as rigid, chemical compounds are flexible three dimensional objects made up of atoms which continuously move and oscillate. Cyrus Levinthal noted already in 1969 that the large amount of degrees of freedom of chemical compounds formally leads to a catastrophically large number of possible conformations well up to 10300 (Levinthal's Paradoxon).