Degenerate stacking configurations of bilayer PtSe2. Using ALCF supercomputing resources, researchers are exploring how near-degenerate stacking configurations affect the electronic structure and layer-dependent metal-insulator transitions in few-layer PtSe2, a key challenge for developing next-generation nanoelectronic devices. Image: ALCF Visualization and Data Analytics Team; Hyeondeok Shin, Argonne National Laboratory
This project will apply benchmark-accuracy quantum Monte Carlo methods to predict and understand the properties of quantum materials, providing reference data for machine-learning approaches and advancing theoretical tools for designing next-generation quantum devices.
One of the most significant challenges in theoretical and computational condensed matter physics is to be able to predict, understand, realize, and optimize desired properties in specific real materials. Quantum materials -- materials that exhibit novel physical properties arising from the quantum mechanics of their electrons and the constituent atoms and structure of their host materials -- are both challenging to model accurately and of critical interest due to their potential uses in quantum sensors, quantum computing devices, and new forms of electronics. This project will apply state of the art benchmark-accuracy quantum Monte Carlo (QMC) methods to meet the above challenges by providing benchmark accuracy predictions and understanding for highly topical quantum materials, and reference data for machine-learning and artificial intelligence-based approaches. Several new theoretical developments will also be pursued to further increase accuracy and performance.
