  A bottleneck for multi-timescale dynamics is the computation of the potential
energy surface (PES). We explore the use of genetic programming (GP) to
symbolically regress a mapping of the saddle-point barriers from only a few
calculated points via molecular dynamics, thereby avoiding explicit calculation
of all the barriers. The GP-regressed barrier function enables use of kinetic
Monte Carlo (KMC) to simulate real-time kinetics (seconds to hours) using
realistic interactions. To illustrate, we apply a GP regression to
vacancy-assisted migration on a surface of a binary alloy and predict the
diffusion barriers within 0.1--1% error using 3% (or less) of the barriers, and
discuss the significant reduction in CPU time.
