Collaborative Research: Elements: Building an open source DFT+eDMFT database for quantum materials

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The discovery of new quantum materials plays a crucial role in technological advancements. The use of data science tools, coupled with artificial intelligence (AI), has the potential to greatly accelerate materials discovery. However, the effectiveness of these tools relies on having large, high-quality material databases. Unfortunately, many existing databases built over the past decade have been inadequate in accurately predicting the properties of quantum materials. Quantum materials exhibit highly correlated quantum mechanical phenomena at the atomic scale, which manifest socially useful properties like magnetism and superconductivity at the macroscopic scale. This project aims to develop an open-source, high-fidelity materials database by implementing high-throughput algorithms using modern quantum many-body methods, which have not yet been employed for large-scale database creation. Additionally, the project enhances existing large-scale materials databases using AI tools. To ensure accessibility, this project will make the infrastructures and tutorials for these databases freely available to the scientific community. Important materials databases include exclusively physical property data created from Density Functional Theory (DFT) engines which describe the physical properties of simple materials in terms of independent electrons in the presence of an average potential. For strongly correlated quantum materials, which cannot be treated in this averaged manner, DFT often fails to predict correct physical properties, while the Dynamical Mean Field Theory (DMFT) approach allows far more accurate calculations of the same properties at a higher but still practical cost. To overcome the DFT-based shortcomings of existing materials databases and to transform data-science-driven materials discovery into a new era, the team aims to build an open-source high-fidelity database of quantum materials properties in which the DFT engine is replaced by the more precise many-body method based on a combination of DFT and DMFT (DFT+DMFT), through the development of a new high-throughput DFT+DMFT workflow. This high-fidelity but the smaller database will be used to correct the existing large-scale DFT databases using artificial intelligence tools, such as transfer learning, allowing the automatic repair of the systematic errors in the less accurate DFT data once the high-fidelity data in a smaller domain is known. This research also aims to expand the knowledge and skills of students in quantum theory, AI careers, and interdisciplinary fields that bridge materials and data science.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.