A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles
<|reference_start|>A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles: Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology was applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalysts. The model's performance in detecting and segmenting NPs was validated across diverse heterogeneous catalyst systems, including various metals (Cu, Ru, Pt, and PtCo), supports (silica ($\text{SiO}_2$), $\gamma$-alumina ($\gamma$-$\text{Al}_2\text{O}_3$), and carbon black), and particle diameter size distributions with means and standard deviations of 2.9 $\pm$ 1.1 nm, 1.6 $\pm$ 0.2 nm, 9.7 $\pm$ 4.6 nm, and 4 $\pm$ 1.0 nm. Additionally, the proposed machine learning (ML) approach successfully detects and segments overlapping NPs anchored on non-uniform catalytic support materials, providing critical insights into their spatial arrangements and interactions. Our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.<|reference_end|>
@article{genc2024a,
    title={A versatile machine learning workflow for high-throughput analysis of
  supported metal catalyst particles},
    author={Arda Genc, Justin Marlowe, Anika Jalil, Libor Kovarik, Phillip
  Christopher},
    journal={arXiv preprint arXiv:2410.01213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01213},
    primaryClass={cond-mat.mtrl-sci cs.AI}
}