About

Mission

A major theme of condensed matter and materials physics is the relationship between the microscopic behavior of electrons and nuclei to the emergent low-energy mesoscopic behavior of materials. Elucidating this relationship is a challenge, since the microscopic model requires advanced solution methods for many-body quantum mechanics, and the mesoscopic picture can be rather complicated. In complex materials, standard concepts at the mesoscopic level such as phonons, spins, and electron-like excitations can interact in complex ways which are difficult to access experimentally.

The state of the art in creating mesoscopic models starting from microscopic behavior is based on density functional theory (DFT) calculations. In recent years, modern machine learning techniques have been able to reproduce potential energy surfaces from standard DFT functionals to a very high accuracy; the accuracy potential energy surfaces can be limited by the underlying data. In quantum materials such as twisted bilayer graphene,(pictured above) interactions between electronic excitations can be critical to their behavior. To resolve the above issues, it is necessary to move beyond density functional theory and to base mesoscopic models on more accurate microscopic calculations. In this project, we will use quantum Monte Carlo calculations as a base for high-impact projects which can benefit from extra accuracy.

Vision

At each length scale of interest, advanced tools exist. Our collaboration contains experts at each of these length scales. Our goal is to systematically link the microscopic to the mesoscopic by using well-defined data interfaces. The highly accurate, sub-atomic scale quantum Monte Carlo calculations produce data, which is then used to understand the physics at the atomic scale, and so on. Reproducibility and documentation are achieved by using modern scientific computing methods.

Acknowledgments

This project is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Computational Materials Sciences program under Award Number DE-SC0020177.

Support of the Materials Research Lab at the University of Illinois is also gratefully acknowledged.