# Special Glass Structures for First Principles Studies of Bulk Metallic   Glasses

**Authors:** Siya Zhu, Jan Schroers, Stefano Curtarolo, Hagen Eckert, Axel van de, Walle

arXiv: 2302.11644 · 2024-01-17

## TL;DR

This paper introduces a method to design small-cell structures that accurately replicate the local geometric descriptors of bulk metallic glasses, enabling efficient property calculations with ab initio methods.

## Contribution

It presents a novel approach combining MD and RMC to optimize small-cell models for bulk metallic glasses, reducing computational costs.

## Key findings

- Successfully reproduces local geometric descriptors of large-cell simulations
- Enables accurate property calculations with smaller, computationally manageable models
- Implemented in the MAST software package for broader use

## Abstract

The atomic-level structure of bulk metallic glasses is a key determinant of their properties. An accurate representation of amorphous systems in computational studies has traditionally required large supercells that are unfortunately computationally demanding to handle using the most accurate ab initio calculations. To address this, we propose to specifically design small-cell structures that best reproduce the local geometric descriptors (e.g., pairwise distances or bond angle distributions) of a large-cell simulation. We rely on molecular dynamics (MD) driven by empirical potentials to generate the target descriptors, while we use reverse Monte Carlo (RMC) methods to optimize the small-cell structure. The latter can then be used to determine mechanical and electronic properties using more accurate electronic structure calculations. The method is implemented in the Metallic Amorphous Structures Toolkit (MAST) software package.

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/2302.11644/full.md

## References

45 references — full list in the complete paper: https://tomesphere.com/paper/2302.11644/full.md

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Source: https://tomesphere.com/paper/2302.11644