Garnet Chan at the California Institute of Technology is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division to develop new computational techniques that will allow scientists to address the inner workings of the biological systems that help convert nitrogen in the air into the chemical forms that plants and animals use. In addition, he is pursuing a fundamental reformulation of quantum mechanics that potentially will enable the quantum mechanics of any physical system to be efficiently simulated on computers. The outreach activities focus on the recruitment of underrepresented undergraduates to pursue summer research with the view towards addressing the pipeline issue in the STEM disciplines.
In more detail, the computational techniques being developed in the first part of the research focus on extending density matrix renormalization group (DMRG) techniques to confidently treat strongly correlated electronic structure as found in systems such as the FeMo cofactor (FeMoCo) of nitrogenase. These techniques include improved DMRG active space methods; time-dependent perturbation theory techniques; and time-dependent spectral methods. In the second part of the research he is pursuing an intensive development program to bring the full generality of tensor network algorithms, across the threshold of models, to ab initio quantum chemistry. This work pursues connections between tensor networks and modern machine learning algorithms.
