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Neural network potential for Zr-Rh system by machine learning.

Kun Xie¹, Chong Qiao², Hong Shen¹

¹Shanghai Ultra-Precision Optical Manufacturing Engineering Center, Department of Optical Science and Engineering, Fudan University, Shanghai, 200433, People's Republic of China.

Journal of Physics. Condensed Matter : an Institute of Physics Journal

|November 9, 2021

Summary

This summary is machine-generated.

Researchers developed a machine learning potential for Zr-Rh metallic glass, improving molecular dynamics simulations. This allows for larger-scale studies of amorphous structures, crucial for material design and applications.

Keywords:

Zr–Rh metallic glass machine learning molecular dynamics neural network potential

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Area of Science:

Materials Science
Computational Materials Science
Amorphous Alloys

Background:

Zirconium-Rhodium (Zr-Rh) metallic glass exhibits excellent mechanical properties, leading to applications in automotive and sports equipment.
Understanding the microstructure-property relationship in Zr-Rh metallic glass is crucial but limited by current simulation capabilities.

Purpose of the Study:

To develop a machine learning-based deep neural network potential for the Zr-Rh system.
To overcome the accuracy-efficiency trade-off in molecular dynamics (MD) simulations for Zr-Rh metallic glass.
To enable large-scale simulations for exploring amorphous structures and melt-quenching processes.

Main Methods:

Developed a deep neural network potential for Zr-Rh using machine learning.
Performed molecular dynamics simulations with the developed potential.
Validated results against *ab initio* molecular dynamics simulations.
Simulated a large model (5400 atoms) to study melt-quenching effects on Zr77Rh23.

Main Results:

The deep neural network potential accurately reproduces structural features compared to *ab initio* MD.
Simulations achieved significantly improved spatial and temporal scales.
Investigated the influence of simulation size and cooling rate on the melt-quenching process of Zr77Rh23.

Conclusions:

The machine learning potential provides an accurate and efficient tool for simulating Zr-Rh metallic glass.
This approach facilitates the exploration of complex amorphous structures in Zr77Rh23.
The findings are significant for the rational design and practical application of Zr-Rh metallic glass.