Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.
A fully autonomous robotic system developed by MIT researchers could speed things up.
Their system utilizes a robotic probe to measure an important electrical property known as photoconductivity, which is how electrically responsive a material is to the presence of light.
The researchers inject materials-science-domain knowledge from human experts into the machine-learning model that guides the robot’s decision making. This enables the robot to identify the best places to contact a material with the probe to gain the most information about its photoconductivity, while a specialized planning procedure finds the fastest way to move between contact points.
During a 24-hour test, the fully autonomous robotic probe took more than 125 unique measurements per hour, with more precision and reliability than other artificial intelligence-based methods.
By dramatically increasing the speed at which scientists can characterize important properties of new semiconductor materials, this method could spur the development of solar panels that produce more electricity.
“I find this paper to be incredibly exciting because it provides a pathway for autonomous, contact-based characterization methods. Not every important property of a material can be measured in a contactless way. If you need to make contact with your sample, you want it to be fast and you want to maximize the amount of information that you gain,” said Senior Author Tonio Buonassisi, Professor of Mechanical Engineering.
His co-authors include Lead Author Alexander (Aleks) Siemenn, Graduate Student; Postdocs Basita Das and Kangyu Ji; and Graduate Student Fang Sheng. The work will appear in Science Advances.
Researchers in Buonassisi’s Accelerated Materials Laboratory for Sustainability are working toward a fully autonomous materials discovery laboratory. They’ve recently focused on discovering new perovskites, which are a class of semiconductor materials used in photovoltaics like solar panels.
In prior work, they developed techniques to rapidly synthesize and print unique combinations of perovskite material. They also designed imaging-based methods to determine some important material properties.
But photoconductivity can only be characterized by placing a probe onto the material, shining a light, and measuring the electrical response.
“To allow our experimental laboratory to operate as quickly and accurately as possible, we had to come up with a solution that would produce the best measurements while minimizing the time it takes to run the whole procedure,” said Siemenn.
Doing so required the integration of machine learning, robotics, and material science into one autonomous system. To begin, the robotic system uses its onboard camera to take an image of a slide with perovskite material printed on it.
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