Machine learning helps solve a central problem of quantum chemistry
By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major breakthrough toward solving a decades-old dilemma in quantum chemistry: the precise and stable calculation of molecular energies and electron densities with a so-called orbital-free approach, which uses considerably less computational power and therefore permits calculations for very large molecules. Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such that it delivers precise results and reliably establishes a physically meaningful solution. The findings are published in the Journal of the American Chemical Society. Why molecular electron densities matter How electrons are distributed in a molecule determines its chemical properties—from its stability and ...
