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Related Experiment Video

Updated: Jun 30, 2026

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Synthesis of Near-Infrared Emitting Gold Nanoclusters for Biological Applications

Published on: March 22, 2020

Active learning-driven global search for neutral gold clusters via neural network potential.

Zhengyu Tu^1,2, Guanchen Dong^2,3, Yuxuan Chen^1,2

¹Beijing Computational Science Research Center, Beijing 100193, China.

Physical Chemistry Chemical Physics : PCCP

|June 29, 2026

Summary

We developed an efficient machine learning framework for predicting metal nanocluster structures. This method accurately identifies low-energy configurations, revealing key structural transitions in gold clusters.

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Area of Science:

Computational chemistry
Materials science
Nanotechnology

Background:

Predicting metal nanocluster structures is computationally challenging due to complex energy landscapes.
First-principles calculations are often too costly for extensive structure prediction.

Purpose of the Study:

To develop an efficient framework for metal nanocluster structure prediction.
To accurately identify low-energy structures of gold nanoclusters (Au_n, n=30-45).

Main Methods:

Integrating machine learning interatomic potentials (MLIPs) with global optimization algorithms.
Iteratively training neural network atomic potentials to density-functional-theory (DFT) accuracy.
Employing a genetic algorithm for exploring complex energy landscapes.

Main Results:

The framework successfully identified low-energy structures for Au_n clusters at reduced computational cost.
A non-monotonic structural evolution was observed, transitioning from hollow cages to multi-core cages.
Significant differences in structural evolution were noted between neutral and anionic gold clusters.

Conclusions:

The proposed active-learning workflow offers an efficient and extensible strategy for metal cluster structure prediction.
The findings highlight the impact of electronic structure on nanocluster morphology.
This approach can be applied to investigate metal clusters with complex electronic properties.