AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere
Abstract Materials Acceleration Platforms (MAPs) – also known as self‐driving laboratories– present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery compared to the traditional trial‐and‐error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, particularly for films fabricated in a humid atmosphere. Here, a machine learning (ML)‐guided closed‐loop platform is developed with a multimodal data fusion approach to predict synthesis–property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5–55%. The efficiency of this approachmore »