Today, the design of functional materials is greatly hindered by the limited predictive power of established quantum mechanics-based approaches. The strong coupling between charge, spin, orbital, and lattice degrees of freedom that results in desired functionalities also challenges established modeling approaches.  

 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 improve on the state of the art. The team is performing calculations on established benchmark materials, as well as new materials systems where the predictions will be validated by new experimental works and characterization. This will provide a stringent and timely validation of the newly developed methods, and advance efforts to identify new functionalities for energy-related technologies.