Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells.

ABSTRACT: Achieving high performance in perovskite solar cells (PSCs) is challenging due to the numerous complex variables involved across multiple fabrication steps. The strong interdependence among design variables, such as fabrication processing parameters and material composition, further complicates the search for optimal designs in a vast design space. In this work, we present a data-driven, algorithm-guided experimentation framework for the systematic optimization of PSC performance. We employed a model-based, derivative-free optimization algorithm to explore the design space of key processing parameters and identify promising regions for experimentation. By optimizing up to six processing parameters across the device structure and requiring fewer than 100 tested designs, we improved reverse-

SUBMITTER: Oh D 

PROVIDER: S-EPMC12706828 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature