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Updated: Feb 24, 2026

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Published on: June 25, 2018

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm.

Ryosuke Jinnouchi¹, Ryoji Asahi¹

¹Toyota Central R&D Laboratories, Inc. , 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan.

The Journal of Physical Chemistry Letters

|August 25, 2017

Summary

This study introduces a machine learning model to predict catalytic activity on alloy nanoparticles by analyzing local atomic structures. This approach overcomes limitations of traditional single-crystal surface models for complex materials.

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Area of Science:

Materials Science
Catalysis
Computational Chemistry

Background:

Heterogeneous catalyst performance relies on specific surface sites.
Traditional methods use single-crystal surfaces, limiting analysis of complex structures like alloy nanoparticles.
Alloy nanoparticles exhibit inhomogeneous atomic configurations and atomic-scale defects.

Purpose of the Study:

To develop a universal machine learning scheme for predicting catalytic activity on alloy nanoparticles.
To enable interrogation of catalytic activities based on local atomic configurations.
To overcome limitations of single-crystal surface models for complex catalytic materials.

Main Methods:

A machine learning scheme utilizing a local similarity kernel.
Application to direct nitrogen monoxide (NO) decomposition on Rhodium-Gold (RhAu) alloy nanoparticles.
Utilizing Density Functional Theory (DFT) data from single crystals to predict nanoparticle energetics.

Main Results:

The machine learning scheme efficiently predicts catalytic reaction energetics on nanoparticles.
Successfully applied to NO decomposition on RhAu alloy nanoparticles.
Demonstrated ability to predict size- and composition-dependent catalytic activities.

Conclusions:

The proposed machine learning method provides a universal approach for analyzing catalytic activities on complex surfaces.
Enables detailed information on active site structures and catalytic performance.
Offers a pathway to designing high-performance heterogeneous catalysts based on nanoparticle characteristics.