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

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.