Multiobjective Optimization of Silver-Nanowire Deposition for Flexible Transparent Conducting Electrodes

Optimizing the spin coating of silver nanowires to form transparent conducting electrodes (TCE) is guided by machine learning (ML). A good TCE has two competing characteristics: high transmittance and high conductance. Optimization using a scalar figure of merit, as often done in the field, cannot satisfy the independent requirements for transmittance and conductance imposed by specific applications. By performing a Pareto front analysis based on ML models, we show that the desired outcomes of transmittance ≥ 75% and sheet resistance ≤ 15 Ω/sq are challenging but can be achieved using processing parameters identified by ML analysis.