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Updated: Jun 28, 2026

A Technique to Functionalize and Self-assemble Macroscopic Nanoparticle-ligand Monolayer Films onto Template-free Substrates

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Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings.

Felipe Hawthorne^1,2, Leandro Seixas³, James M Almeida⁴

¹Department of Physics, Federal University of Paraná, 81530-015 Curitiba, Paraná, Brazil.

The Journal of Physical Chemistry. A

|June 27, 2026

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This summary is machine-generated.

We developed a data-efficient machine learning model to predict stable nanoparticle configurations. This approach uses a layer-resolved descriptor and requires minimal reference calculations for materials discovery.

Area of Science:

Materials Science
Computational Chemistry
Machine Learning

Background:

Nanoparticle stability is complex due to diverse atomic environments.
Identifying stable configurations is challenging with limited data for first-principles methods.

Purpose of the Study:

To introduce a data-efficient machine learning framework for predicting nanoparticle stability.
To enable accurate identification of low-energy configurations using limited reference data.

Main Methods:

A fragmented, layer-resolved descriptor decomposing nanoparticles into surface, intermediate, and core environments.
Gradient-boosted decision-tree models with a ranking-based learning strategy.
Physically motivated weighting schemes and SHAP-based interpretability analyses.

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Preparation of Nanoparticles for ToF-SIMS and XPS Analysis

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Preparation of Nanoparticles for ToF-SIMS and XPS Analysis

Preparation of Nanoparticles for ToF-SIMS and XPS Analysis

Published on: September 13, 2020

Main Results:

Accurate identification of stable nanoparticle configurations using a few hundred density functional theory calculations.
Demonstrated high data efficiency with near-saturation of correlation and high top-k recall.
Revealed contributions of surface segregation, coordination topology, and local disorder to stability.

Conclusions:

The framework accurately predicts nanoparticle stability and is highly data-efficient.
Provides physical insights into factors governing stability across different nanoparticle regions.
The system and code-agnostic approach is transferable to other nanostructures and methods.