Development of a Machine Learning Potential to Study Structure and Thermodynamics of Nickel Nanoclusters

TL;DR

This paper develops a Gaussian Approximation Potential (GAP) model for nickel nanoclusters, demonstrating its ability to accurately predict energetics, structure, and thermodynamics across various sizes, and exploring its extrapolative capabilities and stability insights.

Contribution

The paper introduces a novel GAP model specifically designed for low-dimensional Ni nanoclusters, highlighting its effectiveness and flexibility in capturing complex properties and behaviors.

Findings

GAP model accurately predicts energetics and structures of Ni nanoclusters.

Data-driven models reveal size-dependent phase behavior.

Model demonstrates good extrapolative capabilities to different regimes.

Abstract

Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes, while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large scale MD simulations using the GAP model analyzed with data-driven models like Graph Neural Networks (GNN) reveal intriguing insights into the size-dependent phase behavior and thermo-mechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.