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R2Roptimizer (Process Optimization of Graphene Growth) is an application developed in Jupyter notebook. The application runs chemical vapor deposition system optimization simulations (to optimize graphene growth) using Gaussian process regression surrogate model and expected improvement over dominated hypervolume information acquisition function. The optimization algorithm has been written in Python.
The program consists of two main phases; Input phase and Output phase. The input phase includes uploading the input/output files. The input file contains the process parameters at which the experiments were conducted. The output file has the objectives that are required to be maximized. The two objectives in our case are the IG/ID and I2D/IG ratios from Raman spectroscopy of graphene. The uploaded files must be in CSV format. Once the simulation is done, the output phase displays 3 different types of output; first output is 1-Dimensional projection of high dimension response surface (of the objective function), second is relative dependence of inputs on the outputs and the third output is the value of the input parameters at which the next experiment can be conducted to optimize the objective functions.
NanoHUB Team, Purdue University.
Network for Computational Nanotechnology
Emmerich, M., & Klinkenberg, J. W. (2008). The computation of the expected improvement in dominated hypervolume of Pareto front approximations.Rapport technique, Leiden University.
Pandita, P., Bilionis, I., & Panchal, J. (2016). Extending Expected Improvement for High-dimensional Stochastic Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1604.01147.
Cite this work
Researchers should cite this work as follows:
- Graphene growth
- Gaussian Processes
- Pareto Front
- stochastic simulation