This FWP develops new theories and computational methods to significantly accelerate scientific discoveries to support the mission of the Department of Energy’s Basic Energy Science and of Ames National Laboratory. The proposed research tackles challenging science problems including:
- Develop ab initio theories and computational methods for accurate calculation of real correlated materials containing d and f electrons, without relying on the choice of U and J parameters and with fast computational speed.
- Provide accurate description and prediction of crystal field effects, magnetism, spin fluctuation/dynamics, and interplays among electron correlation and magnetism in quantum materials containing d and f electrons.
- Integrate artificial intelligence/machine learning (AI/ML) with materials modeling/simulation and materials databases to dramatically accelerate the pace of discovery, design, and synthesis of novel functional materials with unique physical properties.
The proposed work aligns well with the strategic plan of Ames National Laboratory on “Fundamental Rare Earth Science” and on “Discovery, design, and scalable synthesis of transformative materials.” The proposed research also responds directly to DOE-BES’s Grand Challenges on, “How do remarkable properties of matter emerge from the complex correlations of atomic or electronic constituents, and how can we control these properties?” and on “Transformative opportunities for discovery science.” Our proposed research also supports the DOE’s mission on Artificial Intelligence for Science initiatives. Success in our research will have significant impacts on advancing the fundamental sciences and materials design and discovery.
Project Members:
Principal Investigator: Cai-Zhuang Wang
Co-PIs: Vladimir Antropov, Yongxin Yao, Feng Zhang