Session

DescriptionThe field of computational materials design has been pushed to new frontiers over the last decade. This is mainly rooted in recent advances to augment well known simulation approaches like density functional theory (DFT) or molecular dynamics (MD) simulations with data-driven approaches like machine learning. The representation of DFT potential energy surfaces with neural networks lead to a great gain in computational efficiency without losing much of the accuracy of the DFT calculation. In addition to describing interatomic interactions, machine learning has been further applied to predict material properties based on first-principle calculations or to classify molecules based on thermodynamic properties. The ultimate goal of this new class of methodological approaches is to aid the development of novel materials. These materials may find applications in drug design, unconventional energy resources or innovative semiconducting materials. This minisymposium will present state-of-the-art examples of the underlying method development, scientific applications, and implementation on high performance computing platforms.