The use of Abell–Tersoff potentials in atomistic simulations of InGaAsSb/GaAs

Haxha, V.; Garg, Raghav; Migliorato, M. A.; Drouzas, I.; Ulloa, JM José Maria; Koenraad, PM Paul; Steer, M. J.; Hy, Liu; Hopkinson, M.; Mowbray, D. J.

doi:10.1007/s11082-009-9276-3

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Many commonly used force fields, such as the modified embedded atom model or Stillinger–Weber potentials, cannot capture bond-making and bond-breaking processes nor any associated phase change. The well-known Tersoff model is a “bond order”-based method and might be considered to offer a compromise in lieu of explicit bond-making and bond-breaking capability, but the limitations of this model have also become well-documented. − A more flexible model, such as ReaxFF, which is explicitly designed to capture reactive systems, is certainly an alternative. In practice, ReaxFF requires extensive parametrization, and the result is highly specific to one particular reactive system. , If models for the system of study exists, then this is a viable option.…”

Section: Introductionmentioning

confidence: 99%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized 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 nonparametric force field produces properties with excellent accuracy as well as exhibits 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 the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

show abstract

Section: Introductionmentioning

confidence: 99%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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show abstract

“…The well-known Tersoff model [3] is a "bond order" based method and might be considered to offer a compromise in lieu of explicit bond-making/breaking capability, but the limitations of this model have also become well-exposed. [4][5][6] Use of a more sophisticated model, such as ReaxFF, which is explicitly designed to capture reactive systems, could be used in theory to capture the reactive nature of a system. In practise, ReaxFF requires extensive parameterization, and the result is highly specific to one particular reactive system.…”

Section: Introductionmentioning

confidence: 99%

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

Clancy

Chen

Shao

et al. 2022

Preprint

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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.

show abstract