Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors

Li, Yuxin; Dong, Rongzhi; Yang, Wenhui; Hu, Jianjun

doi:10.48550/arxiv.2105.07303

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…In our previous work, 4 the crystal structure prediction problem can be mapped to two related problems: (1) prediction of the contact map of atoms; (2) the atomic coordinate reconstruction from the contact map using global optimization algorithms. We have applied both genetic algorithms and differential evolution algorithms 37 for the coordinate reconstruction. However, in both algorithms, the evolving population can easily get trapped in local optima due to premature convergence, when the diversity of the population decreases dramatically after a few generations, leading to the loss of search capability.…”

Section: Methods 21 Coordination Number As An Optimizationmentioning

confidence: 99%

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

Yang

Siriwardane

2022

J. Phys. Chem. A

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Crystal structure prediction (CSP) has emerged as one of the most important approaches for discovering new materials. CSP algorithms based on evolutionary algorithms and particle swarm optimization have discovered a great number of new materials. However, these algorithms based on ab initio calculation of free energy are inefficient. Moreover, they have severe limitations in terms of scalability. We recently proposed a promising crystal structure prediction method based on atomic contact maps, using global optimization algorithms to search for the Wyckoff positions by maximizing the match between the contact map of the predicted structure and the contact map of the true crystal structure. However, our previous contact-map-based CSP algorithms have two major limitations: (1) the loss of search capability due to getting trapped in local optima; (2) it only uses the connection of atoms in the unit cell to predict the crystal structure, ignoring the chemical environment outside the unit cell, which may lead to unreasonable coordination environments. Herein, we propose a novel multiobjective genetic algorithm for contact-map-based crystal structure prediction by optimizing three objectives, including contact map match accuracy, individual age, and coordination number match. Furthermore, we assign the age values to all the individuals of the GA and try to minimize the age, aiming to avoid the premature convergence problem. Our experimental results show that compared to our previous CMCrystal algorithm, our multiobjective crystal structure prediction algorithm (CMCrystalMOO) can reconstruct the crystal structure with higher quality and alleviate the problem of premature convergence. The source code is open sourced and can be accessed at .

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Section: Methods 21 Coordination Number As An Optimizationmentioning

confidence: 99%

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

Yang

Siriwardane

2022

J. Phys. Chem. A

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our previous work [4], the crystal structure prediction problem can be mapped to two related problems: 1) prediction of the contact map of atoms; 2) the atomic coordinate reconstruction from the contact map using global optimization algorithms. We have applied both genetic algorithms and differential evolution algorithms [37] for the coordinate reconstruction. However, we find that in both algorithms, the evolving population can easily get trapped in local optima due to the premature convergence, when the diversity of the population decreases dramatically after a few generations, which leads to the loss of search capability.…”

Section: B Age-fitness Based Multi-objective Genetic Algorithm For Cr...mentioning

confidence: 99%

Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints

Yang,

Siriwardane,

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Crystal structure prediction (CSP) has emerged as one of the most important approaches for discovering new materials. CSP algorithms based on evolutionary algorithms and particle swarm optimization have discovered a great number of new materials. However, these algorithms based on ab initio calculation of free energy are inefficient. Moreover, they have severe limitations in terms of scalability. We recently proposed a promising crystal structure prediction method based on atomic contact maps, using global optimization algorithms to search for the Wyckoff positions by maximizing the match between the contact map of the predicted structure and the contact map of the true crystal structure. However, our previous contact map based CSP algorithms have two major limitations: (1) the loss of search capability due to getting trapped in local optima; (2) it only uses the connection of atoms in the unit cell to predict the crystal structure, ignoring the chemical environment outside the unit cell, which may lead to unreasonable coordination environments. Herein we propose a novel multi-objective genetic algorithms for contact map-based crystal structure prediction by optimizing three objectives, including contact map match accuracy, the individual age, and the coordination number match. Furthermore, we assign the age values to all the individuals of the GA and try to minimize the age aiming to avoid the premature convergence problem. Our experimental results show that compared to our previous CMCrystal algorithm, our multi-objective crystal structure prediction algorithm (CMCrystalMOO) can reconstruct the crystal structure with higher quality and alleviate the problem of premature convergence.

show abstract

Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors

Cited by 2 publications

References 33 publications

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints

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