Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations

Abstract

An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are computationally intractable for a large number of input parameters represented by a high-dimensional input space. It is, therefore, generally unclear as to whether a numerical simulation can reproduce a particular outcome (e.g., a set of experimental results) with a plausible set of model input parameters. Here, we present a method for efficiently searching the input space using Bayesian optimization to minimize the difference between the simulation output and a set of experimental results. Our method allows explicit evaluation of the probability that the simulation can reproduce the measured experimental results in the region of input space defined by the uncertainty in each input parameter. We apply this method to the simulation of charge-carrier dynamics in the perovskite semiconductor methyl-ammonium lead iodide (MAPbI3), which has attracted attention as a light harvesting material in solar cells. From our analysis, we conclude that the formation of large polarons, quasiparticles created by the coupling of excess electrons or holes with ionic vibrations, cannot explain the experimentally observed temperature dependence of electron mobility.