Quantitative knowledge of liquid viscosity is of fundamental importance in many areas of materials synthesis and processing. However, the determination of viscosity often relies on specialized experimental equipment, offline experimentation, or invasive procedures, in particular when required in extreme…
Designing the chemical composition of multi‐component glasses toward a set of target properties requires information on a complex range of trade‐off correlations. Consistent reference datasets are typically not available for this size, which will allow for the training of multi‐task neural network models.…
Large language models (LLMs) are transforming laboratory automation by enabling self-driving laboratories (SDLs) that could accelerate materials research. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental…