Simultaneous Prediction of the Magnetic and Crystal Structure of Materials Using a Genetic Algorithm

Higgins, Edward; Hasnip, Phil; Probert, Matt

doi:10.3390/cryst9090439

Cited by 9 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improved performance of a genetic algorithm for crystal structure and polymorph prediction has been noted by the promotion of diverse structures in a fitness function, suggesting that this approach may have benefits for chemical systems beyond clusters. 66,67 Further insight into the performance of the various predation and fitness operators over the course of the algorithm was obtained by plotting the success of the genetic algorithm as a function of generation. This information is presented in Figure 5, where, for simplicity, only one predation operator (IDCM) is shown, as the energy and IDCM predation operators produced equivalent success versus generation profiles in all cases.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters

Weal

McIntyre

Garden

2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

A structural comparison method (SCM) was created to quantify the structural diversity of nanoclusters and was implemented into a global optimization algorithm to evaluate structural diversity between generated clusters on the fly and promote exploration of the potential energy surface. The SCM evaluated topological differences between clusters using the common neighbor analysis and provided a numerical measure of similarity between the two clusters. The SCM was implemented into a genetic algorithm by integrating it into a new structure + energy fitness operator such that structural diversity of clusters in the population and their energies were used to assign fitness values to clusters. The efficiency of the genetic algorithm with this new fitness operator was benchmarked against several Lennard-Jones clusters (LJ 38 , LJ 75 , and LJ 98 ) known to be difficult cases for global optimization algorithms. For LJ 38 and LJ 75 , this new structure + energy fitness operator performed equally well or better than the energy fitness operator. However, the efficiency of locating the global minimum of LJ 98 decreased using this new structure + energy fitness operator. Further analysis of the genetic algorithm with this fitness operator showed that the algorithm did indeed promote exploration of the PES of LJ 98 as desired but hindered refinement of clusters, preventing it from locating the global minimum even if the energy funnel of the global minimum had been located.

show abstract

Section: ■ Results and Discussionmentioning

confidence: 99%

Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters

Weal

McIntyre

Garden

2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Typically many local minima are discovered during the course of a single CSP run. This can be beneficial, as some CSP searches do not aim to find the global minimum but, rather, metastable species with a particular property such as superhard or magnetic materials. , If the energies/enthalpies of the metastable structures are not too high, it may be possible to synthesize them by appropriately choosing the pressure, temperature, and starting material or by employing synthetic techniques for phases far away from equilibrium …”

Section: Computational Detailsmentioning

confidence: 99%

“…This can be beneficial, as some CSP searches do not aim to find the global minimum but, rather, metastable species with a particular property such as superhard 68 or magnetic materials. 69,70 If the energies/enthalpies of the metastable structures are not too high, it may be possible to synthesize them by appropriately choosing the pressure, temperature, and starting material or by employing synthetic techniques for phases far away from equilibrium. 71 The algorithms that have been adapted toward CSP are wellknown metaheuristics designed to find good solutions to diverse optimization problems ranging from circuit design to protein folding.…”

Section: ■ Computational Detailsmentioning

confidence: 99%

The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

et al. 2020

View full text Add to dashboard Cite

Significant progress has been made in the field of a priori crystal structure prediction, with a number of recent remarkable success stories. Herein, we briefly outline the methods that have been developed for finding the global minimum structure and interesting local minima without the need for experimental information. Focus is placed on describing the XtalOpt evolutionary algorithm (EA) developed in our group toward this end. XtalOpt is published under well-known open-source licenses, and the EA searches can be analyzed via the Avogadro chemical editor and visualizer. We describe new algorithmic developments that have made it possible to predict the structures of ever-more complex crystalline lattices. Benchmark tests, which clearly illustrate how the new developments improve the success rate and accelerate the discovery of the global minimum structure, are performed. Finally, we describe how XtalOpt has been employed to predict novel ternary hydrides that have the propensity for high-temperature superconductivity under pressure.

show abstract

“… 25 In 2019 a GA was designed which performs simultaneously an optimization of the crystal and the magnetic structure. 26 …”

Section: Introductionmentioning

confidence: 99%

Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions

2022

Self Cite

View full text Add to dashboard Cite

We present a systematic study of two widely used material structure prediction methods, the Genetic Algorithm and Basin Hopping approaches to global optimization, in a search for the 3 × 3, 5 × 5, and 7 × 7 reconstructions of the Si(111) surface. The Si(111) 7 × 7 reconstruction is the largest and most complex surface reconstruction known, and finding it is a very exacting test for global optimization methods. In this paper, we introduce a modification to previous Genetic Algorithm work on structure search for periodic systems, to allow the efficient search for surface reconstructions, and present a rigorous study of the effect of the different parameters of the algorithm. We also perform a detailed comparison with the recently improved Basin Hopping algorithm using Delocalized Internal Coordinates. Both algorithms succeeded in either resolving the 3 × 3, 5 × 5, and 7 × 7 DAS surface reconstructions or getting “sufficiently close”, i.e., identifying structures that only differ for the positions of a few atoms as well as thermally accessible structures within k B T /unit area of the global minimum, with T = 300 K. Overall, the Genetic Algorithm is more robust with respect to parameter choice and in success rate, while the Basin Hopping method occasionally exhibits some advantages in speed of convergence. In line with previous studies, the results confirm that robustness, success, and speed of convergence of either approach are strongly influenced by how much the trial moves tend to preserve favorable bonding patterns once these appear.

show abstract

Simultaneous Prediction of the Magnetic and Crystal Structure of Materials Using a Genetic Algorithm

Cited by 9 publications

References 36 publications

Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters

Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters

The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions

Contact Info

Product

Resources

About