Projecting bond properties with machine learning

Researchers have developed a machine learning-based model to predict the characteristics of bonded systems. Using the density of states of the individual component reactants, they have achieved accurate predictions of the binding energy, bond length, number of covalent electrons, and Fermi energy. The broadly applicable model is expected to make a significant contribution to the development of materials such as catalysts and nanowires.