3 papers
cond-mat.mtrl-sci2026
Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves
Eunchi Kim, Thomas Kirchartz
Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends…
physics.comp-ph2025
Towards a fully differentiable digital twin for solar cells
Marie Louise Schubert, Houssam Metni, Jan David Fischbach +18
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…
cond-mat.mtrl-sci2025
Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Eunchi Kim, Paula Hartnagel, Barbara Urbano +2
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 nu…