Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential

Shuang Han, Giovanni Barcaro, Alessandro Fortunelli, Steen Lysgaard, Tejs Vegge, Heine Anton Hansen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The structural stability of nanoalloys is a challenging research subject due to the complexity of size, shape, composition, and chemical ordering. The genetic algorithm is a popular global optimization method that can efficiently search for the ground-state nanoalloy structure. However, the algorithm suffers from three significant limitations: the efficiency and accuracy of the energy evaluator and the algorithm's efficiency. Here we describe the construction of a neural network potential intended for rapid and accurate energy predictions of Pt-Ni nanoalloys of various sizes, shapes, and compositions. We further introduce a symmetry-constrained genetic algorithm that significantly improves the efficiency and viability of the algorithm for realistic size nanoalloys. The combination of the two allows us to explore the space of homotops and compositions of Pt-Ni nanoalloys consisting of up to 4033 atoms and quantitatively report the interplay of shape, size, and composition on the dominant chemical ordering patterns.
Original languageEnglish
Article number121
Journalnpj Computational Materials
Volume8
Issue number1
Number of pages11
ISSN2057-3960
DOIs
Publication statusPublished - 2022

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