The CALYPSO methodology for structure prediction*

Abstract

Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.

High throughput study on magnetic ground states with Hubbard U corrections in transition metal dihalide monolayers

“… 23 Furthermore, since the most stable structure generally locates on the global free energy minimum of the potential energy surface, the corresponding potential energy surface for each TMX 2 will be explored in detail in our further studies by CALYPSO software using the particle swarm optimization (PSO) algorithm. 38 …”

Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

“…Thus, a large chemical space must be sampled to ensure as many congurations as possible are considered and evaluated energetically by electronic structure calculations. Effective computational methods for this purpose fall under the category of global optimization (GO) and include particle swarm optimization, [91][92][93] random searching, 94,95 genetic algorithms, [96][97][98][99][100] basin hopping, [101][102][103] and simulated annealing. 104 In addition, simulation under pressures of gasses and adsorbates, which oen cause signicant restructuring, can be accomplished by minimizing free energy under chemical potentials as opposed to electronic (i.e.…”

Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

“…Thus, a large chemical space must be sampled to ensure as many configurations as possible are considered and evaluated energetically by electronic structure calculations. Effective computational methods for this purpose fall under the category of global optimization (GO) and include particle swarm optimization [78][79][80] , random searching 81,82 , genetic algorithms [83][84][85][86][87] , basin hopping [88][89][90] , and simulated annealing. 91 In addition, simulation under pressures of gasses and adsorbates, which often cause significant restructuring, can be accomplished by minimizing free energy under chemical potentials as opposed to electronic (i.e density functional theory) energy only.…”