Data and code for: Multi-objective Bayesian optimization of AgNW spray-coating for transparent conducting electrodes

This dataset contains the experimental data and Python code used in the manuscript "Multi-objective Bayesian optimization of AgNW spray-coating for transparent conducting electrodes".

Contents:- my_data.csv: Experimental dataset of 95 samples with three input parameters (ultrasonic power, dispersion flow rate, PVP concentration) and measured properties (optical transmittance T, sheet resistance Rₛ).- GPR_AgNW.py: Python code implementing the GPR-based Bayesian optimization framework, including two independent Gaussian process regression models, Pareto front analysis, and Sobol sensitivity analysis.- README.md: Detailed description of files and instructions for running the code.

The code was developed using Python 3.10 with dependencies: numpy, pandas, scikit-learn, matplotlib, SALib. The dataset is provided in CSV format with headers: power, flow_rate, PVP_conc, transmittance, resistance.

This work is licensed under Creative Commons Attribution 4.0 International.