Most of the novel energy materials contain multiple elements occupying a single site in their lattice.The exceedingly large configurational space of these materials imposes challenges in determining low(est)energy str...Most of the novel energy materials contain multiple elements occupying a single site in their lattice.The exceedingly large configurational space of these materials imposes challenges in determining low(est)energy structures.Coulomb energies of possible configurations generally show a satisfactory correlation to computed energies at higher levels of theory and thus allow to screen for minimumenergy structures.Employing an expansion into a binary optimization problem,we obtain an efficient Coulomb energy optimizer using Monte Carlo and Genetic Algorithms.The presented optimization package,GOAC(Global Optimization of Atomistic Configurations by Coulomb),can achieve a speed up of several orders of magnitude compared to existing software.In this work,heuristic optimization on various material classes is performed.Thus,GOAC provides an efficient method for constructing low-energy atomistic models for ionic multi-element materials with gigantic configurational spaces.展开更多
基金The presented work was carried out within the framework of the Helmholtz Association’s program Materials and Technologies for the Energy Transition,Topic 2:Electrochemical Energy Storage.Computation time granted through JARA HPC on the supercomputer JURECA93 at Forschungszentrum Jülich under Grant No.jiek12 is gratefully acknowledged by the authorsK.K.and P.K.thank for the financial support from the“Deutsche Forschungsgemeinschaft”(DFG,German Research Foundation)under project No.501562980.
文摘Most of the novel energy materials contain multiple elements occupying a single site in their lattice.The exceedingly large configurational space of these materials imposes challenges in determining low(est)energy structures.Coulomb energies of possible configurations generally show a satisfactory correlation to computed energies at higher levels of theory and thus allow to screen for minimumenergy structures.Employing an expansion into a binary optimization problem,we obtain an efficient Coulomb energy optimizer using Monte Carlo and Genetic Algorithms.The presented optimization package,GOAC(Global Optimization of Atomistic Configurations by Coulomb),can achieve a speed up of several orders of magnitude compared to existing software.In this work,heuristic optimization on various material classes is performed.Thus,GOAC provides an efficient method for constructing low-energy atomistic models for ionic multi-element materials with gigantic configurational spaces.
