Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm

Abstract

While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (GA) with a neural network inter-atomic potential model to find energetically optimal crystal structures given chemical compositions. We enhance the updated multi-objective GA (NSGA-III) by incorporating the genotypic age as an independent optimization criterion and employ the M3GNet universal inter-atomic potential to guide the GA search. Compared to GN-OA, a state-of-the-art neural potential-based CSP algorithm, ParetoCSP demonstrated significantly better predictive capabilities, outperforming by a factor of 2.562 across 55 diverse benchmark structures, as evaluated by seven performance metrics. Trajectory analysis of the traversed structures of all algorithms shows that ParetoCSP generated more valid structures than other algorithms, which helped guide the GA to search more effectively for the optimal structures. Our implementation code is available at https://github.com/sadmanomee/ParetoCSP.

Keywords

Neural network potential / genetic algorithm / age-fitness / Pareto optimization / crystal structure prediction

References

[1] Blank J, Deb K, Roy PC. Investigating the normalization procedure of NSGA-Ⅲ. Available from: https://www.egr.msu.edu/~kdeb/papers/c2018009.pdf. [Last accessed on 25 Mar 2024]