Radial Basis Functions

Montegranario, Hebert; Espinosa, Jairo

doi:10.1007/978-1-4939-0533-1_8

Cited by 4 publications

(3 citation statements)

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“…SVR models with an RBF employ a kernel function of the form − In eq , the independent variables x are the three factors Cu/(Cu + Zn), reaction time, and concentration of Na 2 EDTA, whereas the dependent variable ( K RBF ) is the corresponding FOM. It is important to consider the values of several hyperparameters: ε specifies the epsilon tube within which no penalty is associated in the training loss function and thus determines support vectors.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Optimization of p-Type Transparent Conducting Films

Wei

Gurudayal

et al. 2019

Chem. Mater.

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P-type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S (CZS) thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multi-dimensional parameter space such that optimization for a specific application is challenging and time consuming. Here, we show that strategic design of experiment (DOE) combined with machine learning (ML) allows for efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of 4 experimental CBD factors. The machine learning model is based on support vector regression (SVR) employing a radial basis function (RBF) as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters.

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Section: Resultsmentioning

confidence: 99%

“…Analysis of variance and factor evaluation from the first round DOE.Cross validation and first round optimization. Support vector regression (SVR) models witha radial basis function (RBF) employs a kernel function of the form:[77][78][79][80] KRBF(x,x') = exp[-x-x'    ]. (2)In Eq.…”

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confidence: 99%

Machine Learning Optimization of p-Type Transparent Conducting Films

Wei

Gurudayal

et al. 2019

Chem. Mater.

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“…Replacing the high-fidelity model by its faster surrogate altogether is an attractive alternative to the methods mentioned in the previous paragraph, as it permits a rapid execution of all types of simulation-based design procedures. Among available modeling techniques, the data-driven surrogates belong to the most popular ones due to their versatility and availability [22][23][24][25] . These surrogates are constructed by approximating the data from the original (here, EM-simulated) model of the system of interest.…”

Section: Introductionmentioning

confidence: 99%