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:ds-8j [version v1]

Publication date: May 09, 2022

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 2022.60 (2022), https://doi.org/10.24435/materialscloud:ds-8j

Description

Atomic-scale simulations of reactive processes have been stymied by two factors: the general 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 suitable to capture the crystallization of gallium nitride (GaN) using a novel additive manufacturing route and a combination of liquid Ga and ammonia gas precursors to grow GaN thin films. We show that this machine learning model is capable of producing a transferable force field that can model all three phases, solid, liquid and gas, involved in this additive manufacturing process. We verified our computational results against a range of experimental measurements and ab initio molecular dynamics simulation, showing that this non-parametric force field shows excellent accuracy as well as a computationally tractable efficiency. The development of this transferable force field opens the opportunity to simulate liquid phase epitaxial growth more accurately than before, 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 mapped Gaussian Process force field parameters of gallium and gallium nitride for LAMMPS simulations. 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
gallium.mgp MD5md5:f5bed28b3c62881bb68b5b1aea6b0986	5.8 MiB	Gallium MGP force field
gallium_nitride_crystal.mgp MD5md5:95a0e5ea5bb411477b1ae843691f9998	46.4 MiB	GaN crystal phase MGP force field
gallium_ntride_liquid.mgp MD5md5:ecaf122f2c7cd789a8f483ae22701a27	48.0 MiB	GaN liquid phase MGP force field
README MD5md5:cb9925fd621ceeba84354b066796bfb1	304 Bytes	README file

License

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External references

Keywords

molecular dynamics machine learning gallium nitride III-V semiconductor