Multi-objective optimization for retinal photoisomerization models with respect to experimental observables

Abstract

The fitting of physical models is often done only using a single target observable. However, when multiple targets are considered, the fitting procedure becomes cumbersome, there being no easy way to quantify the robustness of the model for all different observables. Here, we illustrate that one can jointly search for the best model for each desired observable through multi-objective optimization. To do so we construct the Pareto front to study if there exists a set of parameters of the model that can jointly describe multiple, or all, observables. To alleviate the computational cost, the predicted error for each targeted objective is approximated with a Gaussian process model, as it is commonly done in the Bayesian optimization framework. We applied this methodology to improve three different models used in the simulation of stationary state $cis-trans$ photoisomerization of retinal in rhodopsin. Optimization was done with respect to different experimental measurements, including emission spectra, peak absorption frequencies for the $cis$ and $trans$ conformers, and the energy storage.

12 pages, 14 figures

References in corpus (6)

• Bayesian optimization for tuning and selecting hybrid-density functionals
• Bayesian machine learning for quantum molecular dynamics
• Gaussian Process-Based Refinement of Dispersion Corrections
• Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
• Bayesian optimization for computationally extensive probability distributions
• Bayesian optimization for inverse problems in time-dependent quantum dynamics