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Related Experiment Video

Updated: Sep 13, 2025

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Rapid Atomic Structure Prediction of Multimetallic Nanoparticles with Physics-Based Machine Learning.

Bassel Alkhatib1, Maya Salem1, Klaertje Kiyora Hesselink1

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This study presents a rapid and accurate computational method for predicting the chemical ordering and stability of multimetallic nanoparticles (NPs). The approach optimizes NP design for enhanced performance in catalysis, biomedicine, and electronics.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Nanotechnology

Background:

  • Metal nanoparticles (NPs) are vital in catalysis, biomedicine, and electronics due to tunable properties.
  • Chemical ordering in multimetallic NPs significantly impacts their stability and performance.
  • Traditional methods like Density Functional Theory (DFT) are computationally intensive and limited for complex NPs.

Purpose of the Study:

  • To develop and validate a rapid, accurate computational method for predicting the chemical ordering and stability of multimetallic NPs.
  • To establish guidelines for applying a physics-based model (Bond-Centric Model) coupled with a genetic algorithm.
  • To facilitate the design of stable multimetallic NPs with predictable atomic distributions.

Main Methods:

  • Utilized a physics-based Bond-Centric Model integrated with a genetic algorithm to optimize NP chemical ordering.
  • Calculated weighting factors scaling monometallic bond strengths for bimetallic interactions.
  • Applied the method to 2869-atom cuboctahedron NPs across 15 bimetallic combinations and 6 trimetallic systems.

Main Results:

  • The method accurately predicts chemical ordering and stability for diverse bimetallic NP compositions.
  • Using small metal dimers for weighting factor calculation ensures computational efficiency and accuracy.
  • Successfully extended the model to predict ordering in complex trimetallic NP systems.

Conclusions:

  • The developed methodology offers a computationally efficient and accurate approach for multimetallic NP design.
  • This facilitates the creation of thermodynamically stable NPs with controlled core-to-surface metal atom distribution.
  • Enables accelerated discovery and optimization of NPs for advanced nanotechnological applications.