Stoops Tom, De Backer Annick, Lobato Ivan, Van Aert Sandra
EMAT, University of Antwerp, Groenenborgerlaan 171, Antwerp 2020, Belgium.
NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, Antwerp 2020, Belgium.
Microsc Microanal. 2025 Feb 17;31(1). doi: 10.1093/mam/ozae090.
The Bayesian genetic algorithm (BGA) is a powerful tool to reconstruct the 3D structure of mono-atomic single-crystalline metallic nanoparticles imaged using annular dark field scanning transmission electron microscopy. The number of atoms in a projected atomic column in the image is used as input to obtain an accurate and atomically precise reconstruction of the nanoparticle, taking prior knowledge and the finite precision of atom counting into account. However, as the number of parameters required to describe a nanoparticle with atomic detail rises quickly with the size of the studied particle, the computational costs of the BGA rise to prohibitively expensive levels. In this study, we investigate these computational costs and propose methods and control parameters for efficient application of the algorithm to nanoparticles of at least up to 10 nm in size.
贝叶斯遗传算法（BGA）是一种强大的工具，用于重建使用环形暗场扫描透射电子显微镜成像的单原子单晶金属纳米颗粒的三维结构。图像中投影原子列中的原子数量被用作输入，以获得纳米颗粒的精确且原子级精确的重建，同时考虑先验知识和原子计数的有限精度。然而，由于用原子细节描述纳米颗粒所需的参数数量随着所研究颗粒的尺寸迅速增加，BGA的计算成本上升到令人望而却步的昂贵水平。在本研究中，我们研究了这些计算成本，并提出了方法和控制参数，以便将该算法有效地应用于尺寸至少达10纳米的纳米颗粒。
