A Generalized Force-Modified Potential Energy Surface For Mechanochemical Simulations

We describe the modifications that a spatially varying external load produces on a Born-Oppenheimer potential energy surface (PES) by calculating static quantities of interest. The effects of the external loads are exemplified using electronic structure calculations (at the HF/6-31G- level) of two different molecules: ethane and hexahydro-1,3,5-trinitro-s-triazine (RDX). The calculated transition states and Hessian matrices of stationary points show that spatially varying external loads shift the stationary points and modify the curvature of the PES, thereby affecting the harmonic transition rates by altering both the energy barrier as well as the prefactor. The harmonic spectra of both molecules are blueshifted with increasing compressive pressure. Some stationary points on the RDX-PES disappear under application of the external load, indicating the merging of an energy minimum with a saddle point

Multiobjective genetic algorithms for multiscaling excited state direct dynamics in photochemistry

This paper studies the effectiveness of multiobjective genetic and evolutionary algorithms in multiscaling excited state direct dynamics in photochemistry via rapid reparameterization of semiempirical methods. Using a very limited set of ab initio and experimental data, semiempirical parameters are reoptimized to provide globally accurate potential energy surfaces, thereby eliminating the need for full-fledged ab initio dynamics simulations, which are very expensive. Through reoptimization of the semiempirical methods, excited-state energetics are predicted accurately, while retaining accurate ground-state predictions. The results show that the multiobjective evolutionary algorithm consistently yields solutions that are significantly better—up to 230 % lower error in the energy and 86.5 % lower error in the energy-gradient—than those reported in the literature. Multiple high-quality parameter sets are obtained that are verified with quantum dynamical calculations, which show near-ideal behavior on critical and untested excited state geometries. The results demonstrate that the reparameterization strategy via evolutionary algorithms is a promising way to extend direct dynamics simulations of photochemistr

Multiobjective genetic algorithms for multiscaling excited state direct dynamics in photochemistry

This paper studies the effectiveness of multiobjective genetic and evolutionary algorithms in multiscaling excited state direct dynamics in photochemistry via rapid reparameterization of semiempirical methods. Using a very limited set of ab initio and experimental data, semiempirical parameters are reoptimized to provide globally accurate potential energy surfaces, thereby eliminating the need for fullfledged ab initio dynamics simulations, which are very expensive. Through reoptimization of the semiempirical methods, excited-state energetics are predicted accurately, while retaining accurate ground-state predictions. The results show that the multiobjective evolutionary algorithm consistently yields solutions that are significantly better—up to 230 % lower error in the energy and 86.5 % lower error in the energy-gradient—than those reported in the literature. Multiple high-quality parameter sets are obtained that are verified with quantum dynamical calculations, which show near-ideal behavior on critical and untested excited state geometries. The results demonstrate that the reparameterization strategy via evolutionary algorithms is a promising way to extend direct dynamics simulations of photochemistry to multi-picosecond time scales