Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy
Byung Do Lee, Jin-Woong Lee, Joonseo Park, Min Young Cho, Woon Bae Park, Kee‐Sun Sohn
Abstract
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.
Topics & Concepts
Bayesian optimizationRandom searchCuckoo searchHarmony searchParticle swarm optimizationComputer scienceMetaheuristicMathematical optimizationDensity functional theoryCoulombSelection (genetic algorithm)Ab initioAlgorithmChemistryPhysicsComputational chemistryArtificial intelligenceMathematicsQuantum mechanicsElectronMachine Learning in Materials ScienceInorganic Chemistry and MaterialsX-ray Diffraction in Crystallography