Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles
Abstract
We present the development and applications of a quadratic Spectral Neighbor Analysis Potential (q-SNAP) for ferromagnetic cobalt. Trained on Density Functional Theory calculations using the Perdew-Burke-Ernzerhof (DFT-PBE) functional, this machine-learned potential enables simulations of large systems over extended time scales across a wide range of temperatures and pressures at near DFT accuracy. It is validated by closely reproducing the phonon dispersions of hexagonal close-packed (hcp) and face-centered cubic (fcc) Co, surface energies, and the relative stability of nanoparticles of various shapes. An important feature of this novel potential is its numerical stability in long molecular dynamics simulations. This robustness is exploited to compute the heat capacity of nanoparticles containing up to 9201 atoms, showing convergence to less than 2 J.K-1.mol-1 after 100 ns. Computations of the melting temperature of nanoparticles as a function of their size revealed a convergence to the bulk limit in excellent agreement with the experimental value. Thus, the new, highly accurate machine-learned potential for Co opens exciting opportunities for further applications such as the dynamics of nanoparticles in catalytic reactions.
AI-Generated Overview
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Research Focus: The study presents a quadratic Spectral Neighbor Analysis Potential (q-SNAP) specifically developed for ferromagnetic cobalt, aimed at modeling its properties from bulk to nanoparticles with high accuracy.
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Methodology: The q-SNAP was trained using data derived from Density Functional Theory (DFT) calculations, integrating a diverse set of structures, including bulk cobalt, surfaces, and nanoparticles, to capture various atomic environments.
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Results: The q-SNAP successfully reproduced key properties such as phonon dispersions, surface energies, vacancy formation energies, heat capacities, and melting temperatures of cobalt nanoparticles, demonstrating remarkable agreement with experimental data.
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Key Contributions: This research introduces a highly accurate and numerically stable machine-learned potential for cobalt that allows for extensive molecular dynamics simulations, enabling researchers to study complex systems over long timescales.
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Significance: The findings highlight the potential of the q-SNAP in providing accurate atomic-scale simulations, which are crucial for understanding dynamic processes, especially in catalysis where cobalt nanoparticles are commonly employed.
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Broader Applications: The new potential paves the way for detailed investigations in areas such as catalysis, where understanding the dynamics of nanoparticles can lead to the design of more efficient catalytic materials, alongside potential applications in materials science for related transition metals.