Abstract
When constructing a partially occupied model structure for use in density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. Random sampling and the ensuing low-Coulomb-energy entry selection have been routine. Here, we report a more efficient way of selecting low-Coulomb-energy configurations for a representative solid electrolyte, Li(6)PS(5)Cl. Metaheuristics (genetic algorithm, particle swarm optimization, cuckoo search, and harmony search), Bayesian optimization, and modified deep Q-learning are utilized to search the large configurational space. Ten configuration candidates that exhibit relatively low Coulomb energy values and thereby lead to more convincing DFT and AIMD calculation results are pinpointed along with computational cost savings by the assistance of the above-described optimization algorithms, which constitute an integrated optimization strategy. Consequently, the integrated optimization strategy outperforms the conventional random sampling-based selection strategy.