Computational Methods for Structure Prediction and Solid/Liquid Interfaces for Energy Materials

Richard Hennig
Cornell University

Our research focuses on the development of new methods and algorithms to discover materials and to describe realistic heterogeneous interfaces and the application of these methods to the discovery and design of novel two-dimensional materials for application in energy technologies. The first part of the talk describes our genetic algorithm based approach ( for the discovery of the structure and composition of compounds without any prior information about the system. The application of this method to the Li-Si battery anode material, illustrates how genetic algorithms can efficiently predict a battery materials’ voltage profile. Additionally, the genetic algorithm identifies a previously unreported member of the Li-Si binary phase diagram with composition Li5Si2, which is stable at low temperatures. The second part of the talk introduces our recent development of methods for heterogeneous solid/liquid interfaces in quantum Monte Carlo and density-functional methods. We developed and implemented a new implicit solvation method for quantum Monte Carlo and plane-wave density-functional theory that avoids thermodynamic sampling or explicit solvent electrons. The method is based on a rigorous statistical treatment of the solvent and utilizes a variational theorem. We benchmark the method on solvation energies of small molecules and apply it to the surface energies of metallic and semiconducting nanocrystals. The method is applicable to a variety of challenges in materials science ranging from transition states of solvated molecules to surface reactions in liquid environments.

Presentation (PDF File)

Back to Workshop III: Batteries and Fuel Cells