We develop quantum chemistry and embedding methods for lattice models, molecular systems, and periodic crystals. Our approaches, including density matrix embedding theory (DMET) and dynamical mean-field theory (DMFT), enable the treatment of strong correlations with modest computational cost. These methods have been successfully applied to study the equation of state, magnetic properties, and excitation spectra of strongly correlated materials.
We develop efficient, material-specific numerical methods to investigate exotic quantum phases, including superconductivity, magnetism, charge/spin density waves, and multiferroic phases. Our many-body simulations help elucidate the causal relationships between a material's composition and structure and the emergence of its quantum phases.
Light-matter (electron-photon) and electron-phonon interactions offer powerful ways to understand and control chemical reactions and stabilize quantum phases. We develop approaches that combine canonical transformations and quantum chemistry methods to accurately simulate electron-boson coupled systems.
By combining ab initio simulations with machine learning techniques, such as high-throughput screening and reinforcement learning, we design quantum materials with unprecedented accuracy and efficiency while extending traditional methods to much larger scales.
