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A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning

Xiangyu Chen1*, 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), doi: 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.

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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

Files and data are licensed under the terms of the following license: MIT License.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Manuscript where the data is discussed)
X. Chen, W. Shao, N. Le, P. Clancy, npj Computational Materials, (in preparation)

Keywords

molecular dynamics machine learning gallium nitride III-V semiconductor

Version history:

2022.60 (version v1) [This version] May 09, 2022 DOI10.24435/materialscloud:ds-8j