Abstract:
Chemical processes exhibit chaotic, high-dimensional dynamics as molecules undergo reactions and diffusion. In the special case of a closed, isolated system, the complex dynamical processes relax into a comparatively simple equilibrium steady-state probability distribution. When the stochastic chemical kinetics describes a nonequilibrium process, how can we computationally study the steady state? The traditional answer is to sample trajectories. In this talk, I will discuss how the tensor network techniques (DMRG & TDVP) from quantum many-body problems are naturally repurposed to study many-body stochastic chemical kinetics.
https://chemistry.northwestern.edu/people/core-faculty/profiles/todd-gingrich.html