Enhancing Crystal Structure Prediction by decomposition methods based on graph theory
arXiv:2102.09888 · doi:10.1016/j.fmre.2021.06.005
Abstract
Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows exponentially with system size. In this work, we proposed two crossover-mutation schemes based on graph theory to accelerate the evolutionary structure searching. These schemes can detect molecules or clusters inside periodic networks using quotient graphs for crystals and the decomposition can dramatically reduce the searching space. Sufficient examples for the test, including the high pressure phases of methane, ammonia, MgAl2O4, and boron, show that these new evolution schemes can obviously improve the success rate and searching efficiency compared with the standard method in both isolated and extended systems.
arXiv comment22 pages, 4 figures, 3 tables
References in corpus (24)
- Community structure in social and biological networks
- Finding and evaluating community structure in networks
- Community detection in graphs
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
- Crystal Structure Prediction via Particle Swarm Optimization
- Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds
- Ab initio Random Structure Searching
- Ionic high-pressure form of elemental boron
- Predicting crystal structures: the Parrinello-Rahman method revisited
- Unexpected stable stoichiometries of sodium chlorides
- Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
- Data-driven learning of total and local energies in elemental boron
- Families of superhard crystalline carbon allotropes induced via cold-compressed graphite and nanotubes
- A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure searching
- Constrained evolutionary algorithm for structure prediction of molecular crystals: methodology and applications
- Efficient global structure optimization with a machine learned surrogate model
- Complex low energy tetrahedral polymorphs of group IV elements from first-principles
- Accelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface
- Theoretical prediction of a low-energy Stone-Wales graphene with intrinsic type-III Dirac-cone
- A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation
- Revealing and exploiting hierarchical material structure through complex atomic networks
- Unexpected stable stoichiometries of sodium chlorides
- Dimensionalities and multiplicities determination of crystal nets