Xiangyu Chen^1*, William Shao¹, Nam Le², Paulette Clancy¹

1 Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States

2 Applied Physics Laboratory at Johns Hopkins University, Laurel, Maryland, United States

* Corresponding authors emails: xchen150@jhu.edu

DOI10.24435/materialscloud:57-cj [version v3]

Publication date: May 23, 2023

How to cite this record

Xiangyu Chen, William Shao, Nam Le, Paulette Clancy, A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning, Materials Cloud Archive 2023.78 (2023), https://doi.org/10.24435/materialscloud:57-cj

Description

Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semi-empirical force field on the one hand, and the impractically large computational burden of using ab initio molecular dynamics on the other. In this paper, we use an "on-the-fly" active learning technique to develop a non-parameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this non-parametric force field produces properties with excellent accuracy as well as exhibiting a computationally tractable efficiency. The force field is capable of allowing us to simulate the solid/liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate liquid phase epitaxial growth more accurately than before, to analyze reaction and diffusion processes, and ultimately establish a growth model of the additive manufacturing process to create gallium nitride thin films. In this archive, we included the LAMMPS compatible force field parameters of gallium nitride developed with FLARE++. Users can download these force field parameters to test and recreate similar Molecular Dynamic simulation discussed in the paper.

Materials Cloud sections using this data

Files

File name	Size	Description
gan.flare MD5md5:cb7fc5f6cc955f60b45270ac787e203f	10.7 MiB	FLARE pair style for gallium nitride and gallium, nitrogen species involved in the liquid phase crystallization process
README MD5md5:1bba866d938b125170a69a1dd08bca0a	455 Bytes	README file

License

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External references

Keywords

molecular dynamics machine learning gallium nitride III-V semiconductor