Develop density-functional embedding methods for accurate electronic structure simulations of large systems
Accurate predictions of material properties are important for discovering novel materials. We are actively developing quantum mechanical embedding methods to scale up accurate KS-DFT simulations for obtaining sufficiently accurate electronic properties in large, heterogeneous materials. We have developed the embedding cluster density approximation (ECDA)[1] which provides an effective and simple way to scale up high-level density functional theory (DFT) calculations. ECDA is a local correlation method formulated in the framework of DFT. ECDA is a nearly ''black-box'' method that can be applied to materials having different types of bonds, such as ionic, metallic, and covalent bonds. Another feature of ECDA is that analytical forces can be efficiently computed.[2]
We are currently developing ECDA in the ABINIT program and plan to employ ECDA to understand and even predict the electronic structures of large-scale strongly correlated materials, such as interfacial superconductivity and electronic structures at oxides interfaces. A reliable understanding of these systems will help us improve existing electronic devices and also help us develop new high-performance electronic devices.
Discussions on the ECDA method and related developments are given in following links:
[1] C. Huang, J. Chem. Theory Comput., 14, 6211 (2018) [link]
[2] C. Huang, J. Chem. Phys. 151, 134101 (2019) [link]
[3] Y-C. Chi, M. S. Tameh, C. Huang, J. Chem. Theory Comput., 17, 2737 (2021) [link]
Define clusters for atoms in benzene. For each atom, its nearest neighbours are chosen for defining its cluster. Benzene's electron density is then partitioned among the clusters and their corresponding environments. Yellow: cluster densities. Blue: environment densities.