Predictive Simulations of Functional Materials

PI Paul Kent, Oak Ridge National Laboratory
Diffusion Monte Carlo spin density difference between bulks of potassium-doped nickel oxide and pure nickel oxide, showing the effects of substituting a potassium atom (center atom) for a nickel atom on the spin density of the bulk.
Project Description

Our ability to understand, design, or optimize functional materials is hindered by the limited predictive power of established quantum mechanics-based approaches for important classes of functional materials. In these materials, the strong coupling between charge, spin, orbital, and lattice degrees of freedom that results in desired functionalities requires highly accurate calculations.   

This project supports DOE’s Center for Predictive Simulation of Functional Materials, which focuses on the development, application, validation, and dissemination of parameter-free methods and open source codes to predict and explain the properties of functional materials for energy applications.  Using the open source QMCPACK code, the researchers are demonstrating and validating new quantum Monte Carlo (QMC) methods and algorithms that will significantly advance the state of the art. The team is performing calculations on challenging new materials systems in coordination with new experimental synthesis and characterization. This project will continue to advance efforts to identify new functionalities for energy-related technologies.