Automated Chemistry Combines Chemical Robotics and AI to Accelerate Pace for Advancing Solar Energy Technologies

Researchers at the Department of Energy’s Oak Ridge National Laboratory and the University of Tennessee are automating the search for new materials to advance solar energy technologies.

A new workflow combines robotics and machine learning to study metal halide perovskites, or MHPs — thin, lightweight, flexible materials with outstanding properties for harnessing light that can be used to make solar cells, energy-efficient lighting and sensors.

The study, aims to identify the most stable MHP materials for device integration. The enormous potential for perovskites presents an inherent obstacle for materials discovery. Scientists face a vast design space in their efforts to develop more robust models. More than a thousand MHPs have been predicted, and each of these can be chemically modified to generate a near limitless library of possible compositions.

The synthesis step employed a programmable pipetting robot designed to work with standard 96-well micro plates. The machine saves time over manually dispensing many different compositions; and it minimizes error in replicating a tedious process that needs to be performed in exactly the same ambient conditions, a variable that is difficult to control over extended periods.

Next, researchers exposed samples to air and measured their photo luminescent properties using a standard optical plate reader. Repeating the process over several hours captured complex phase diagrams in which wavelengths of light vary across compositions and evolve over time.

The team developed a machine-learning algorithm to analyse the data and home in on regions with high stability.  While the study focuses on materials discovery to identify the most stable compositions, the workflow could also be used to optimize material properties for specific optoelectronic applications.

The automated process can be applied to any solution-processable material for time and cost savings over traditional synthesis methods.

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