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Atom Probe Tomography Studies on the CuIn,GaSe2 Grain Boundaries

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On coarse projective integration for atomic deposition in amorphous systems.

Claire Y Chuang¹, Sang M Han², Luis A Zepeda-Ruiz³

¹Department of Chemical and Biomolecular Engineering, University of Pennsylvania, 220 South 33rd Street, 311A Towne Building, Philadelphia, Pennsylvania 19104, USA.

The Journal of Chemical Physics

|October 10, 2015

Summary

This study introduces a novel "lifting" scheme for coarse projective integration, enabling accurate molecular dynamics simulations of atomic deposition. The method efficiently simulates complex systems like Germanium deposition on silicon dioxide substrates.

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Area of Science:

Materials Science
Computational Physics
Surface Science

Background:

Direct molecular dynamics simulations of atomic deposition face challenges due to wide time scales.
Existing simulation methods often involve trade-offs between accuracy, complexity, and computational cost.

Purpose of the Study:

To develop an efficient simulation technique for atomic deposition under realistic conditions.
To address the limitations of current simulation approaches by balancing model fidelity and computational efficiency.

Main Methods:

Utilized coarse projective integration, an "equation-free" framework.
Employed periodic short atomistic simulations to compute time derivatives of coarse variables.
Developed a "lifting" scheme to recreate atomistic configurations from coarse variables for Ge deposition on SiO2.

Main Results:

Successfully generated realistic atomistic configurations for Ge islands on amorphous SiO2 substrates.
The lifting scheme accurately recreated configurations using island size distribution measures.
Demonstrated the ability to restart molecular dynamics simulations at any point in time.

Conclusions:

The proposed lifting scheme enables accurate application of coarse projective integration for morphologically complex systems.
This approach significantly enhances the computational efficiency of simulating atomic deposition processes.
Facilitates the study of realistic material growth phenomena previously limited by computational constraints.