arXiv subjects

Eunchi Kim

Publications and source records attributed to Eunchi Kim.

3 recordsLinked to original sources

arXiv2026

Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves

Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends on underlying assumptions such as the choice of one-dimensional drift-diffusion model and selection of free material parameters. To validate the inferred parameters, we perform predictive modeling of light-intensity-dependent current-voltage (JV) characteristics. Well-known physical effects, including the influence of external resistance and recombination dynamics on illumination-dependent device performance, are reflected in parameter estimation and prediction workflow. We show that correct treatment of dark shunt resistance and emphasizing shifted current (J + Jsc) during fitting enhances prediction accuracy at low to intermediate illumination level. Additionally, we analyze the information content of various input JV curve combinations, demonstrating that including at least one illuminated JV, preferably not under high illumination due to series resistance effects, is critical for reliable parameter estimation and device performance prediction.

cond-mat.mtrl-sci↗

arXiv2025

Towards a fully differentiable digital twin for solar cells

Maximizing energy yield (EY) - the total electric energy generated by a solar cell within a year at a specific location - is crucial in photovoltaics (PV), especially for emerging technologies. Computational methods provide the necessary insights and guidance for future research. However, existing simulations typically focus on only isolated aspects of solar cells. This lack of consistency highlights the need for a framework unifying all computational levels, from material to cell properties, for accurate prediction and optimization of EY prediction. To address this challenge, a differentiable digital twin, Sol(Di)$^2$T, is introduced to enable comprehensive end-to-end optimization of solar cells. The workflow starts with material properties and morphological processing parameters, followed by optical and electrical simulations. Finally, climatic conditions and geographic location are incorporated to predict the EY. Each step is either intrinsically differentiable or replaced with a machine-learned surrogate model, enabling not only accurate EY prediction but also gradient-based optimization with respect to input parameters. Consequently, Sol(Di)$^2$T extends EY predictions to previously unexplored conditions. Demonstrated for an organic solar cell, the proposed framework marks a significant step towards tailoring solar cells for specific applications while ensuring maximal performance.

physics.comp-ph↗

arXiv2025

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models

Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity of the parameter space and discard valuable information from suboptimal simulations. In this study, we introduce a novel workflow for parameter estimation in organic solar cells based on a combination of numerical simulations and neural networks. The workflow begins with the selection of an appropriate experimental dataset, followed by the definition of a device model that accurately describes the experiment. To reduce computational complexity, the number of variable parameters is carefully selected, and reasonable ranges are set for each parameter. Instead of directly fitting the experimental data using a numerical model, a neural network was trained on a large dataset of simulated results, allowing for efficient exploration of the high-dimensional parameter space. This approach not only accelerates the parameter estimation process but also provides valuable insights into the likelihood and uncertainty of the estimated parameters. We demonstrate the effectiveness of this method on organic solar cells based on the PBDB-TF-T1:BTP-4F-12 material system, demonstrating the potential of machine learning for rapid and comprehensive characterization of emerging photovoltaic materials.

cond-mat.mtrl-sci↗