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Updated: May 31, 2025
08:55
Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
Published on: June 7, 2018
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Slowly quenched, high pressure glassy B2O3 at DFT accuracy
Debendra Meher1, Nikhil V S Avula1, Sundaram Balasubramanian1
1Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
The Journal of Chemical Physics
|January 24, 2025
Summary
A new machine learning potential (MLP) accurately models B2O3 glass, enabling realistic simulations. This approach overcomes limitations in traditional methods, revealing structural artifacts at high quenching rates.
Area of Science:
- Materials Science
- Computational Chemistry
- Condensed Matter Physics
Background:
- Accurate modeling of inorganic glasses necessitates precise interatomic interactions, large system sizes for structural order, and slow quenching rates.
- Traditional molecular dynamics (MD) simulations, including first-principles and force-field methods, face challenges in meeting these criteria simultaneously.
Purpose of the Study:
- To develop a machine learning potential (MLP) for B2O3 glass that effectively addresses the limitations of existing simulation techniques.
- To enable accurate simulations of glass properties and structure, particularly concerning quenching rates and pressure effects.
Main Methods:
- Development of a machine learning potential (MLP) trained on quantum density functional theory (DFT) data for B2O3.
- Deep potential molecular dynamics (MD) simulations utilizing the developed MLP.
- Comparison of simulation results with experimental data, including equation of state, structure factors, and high-pressure behavior.
Main Results:
- The developed MLP accurately predicts the equation of state and densification of B2O3 glass, especially with slower quenching rates.
- Simulations reveal that quenching rates exceeding 10^11 K/s introduce structural artifacts at ambient conditions.
- Pressure-dependent structure factors from simulations show excellent agreement with experimental x-ray and neutron scattering data.
- High-pressure simulations accurately capture the varied coordination geometries of boron and oxygen.
Conclusions:
- The machine learning potential provides a robust and accurate method for simulating B2O3 glass, overcoming limitations of conventional MD approaches.
- The study highlights the critical influence of quenching rates on glass structure and the validity of the MLP in capturing these effects.
- The findings offer a pathway for more reliable computational studies of glass materials under various conditions.
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