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Efficient crystal structure prediction using universal neural network potential with diversity preservation in genetic algorithms

Takuya Shibayama1, Hideaki Imamura1, Katsuhiko Nishimra1, Kohei Shinohara1,*, Chikashi Shinagawa1, So Takamoto1, and Ju Li2

  • *Contact author: kshinohara@preferred.jp
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Phys. Rev. Materials 10, 063401 – Published 2 June, 2026

DOI: https://doi.org/10.1103/4knt-ccqg

Abstract

Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicomponent systems remains a significant challenge. Here, we propose an improvement of a genetic algorithm (GA)-based CSP method using a universal NNP designed to efficiently expand convex hull volumes while preserving the diversity of crystal structures. Our hull-informed filtering and elitist-selection procedures incorporate an aging mechanism that prioritizes recently improved compositions. We also employ niching to prevent convergence to a small set of stoichiometries, thereby preserving a diverse, high-quality population. Our evaluation shows that the present method outperforms the symmetry-aware random structure generation and existing CSP methods, achieving a larger convex hull with fewer trials. We demonstrated that our approach, combined with the developed universal NNP (PFP), can accurately reproduce and explore phase diagrams obtained through DFT calculations; this indicates the validity of PFP across a wide range of crystal structures and element combinations. This study, which integrates a universal NNP with a GA-based CSP method, highlights the promise of these methods in materials discovery.

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Phys. Rev. Materials 10, 063401– Published 2 June, 2026
  • Received 27 March 2025
  • Revised 23 June 2025
  • Accepted 11 May 2026
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