From Trial-and-Error to Learn-by-Experiment: The Future of Materials Research in Perovskite and Organic Photovoltaics

The next major change in perovskite and organic photovoltaic research may not come from a new material or device architecture, but from a new way of doing and analyzing experiments. Conventional trial-and-error optimization, often a onevariable-at-a-time (OVAT) approach, typically varies a limited number of parameters sequentially, making it difficult to resolve interactions within the increasingly complex processing spaces of complex, multilayer solar cells and demands a large number of experiments. We argue that robotic experimentation combined with machine learning can fundamentally alter this approach. The key advantage of robotics is not simply higher throughput, but highly precise and reproducible fabrication together with comprehensive recording of processing and environmental conditions. This transforms individual experiments into reliable, machine-readable data points and provides the foundation for machine learning to explore multidimensional parameter spaces, identify hidden correlations and autonomously select informative experiments. Optimization can consequently move beyond maximizing power-conversion efficiency toward understanding how parameters interact, with simultaneous consideration of efficiency, lifetime, stability, reproducibility, resource consumption and environmental impact. Such a transition could replace sequential trial-and-error optimization with reproducible, data-rich and closed-loop experimentation. Rather than replacing researchers, robotic laboratories may enable them to investigate materials and processing spaces that are too complex to explore manually. We propose that the long-term impact of autonomous experimentation in photovoltaics will therefore be measured by how much more can be learned from every experiment.