'Self-driving' lab speeds up research, synthesis of energy materials


Date:
March 16, 2022
Source:
North Carolina State University
Summary:
Researchers have developed and demonstrated a 'self-driving lab'
that uses artificial intelligence and fluidic systems to advance
our understanding of metal halide perovskite nanocrystals. This
self-driving lab can also be used to investigate a broad array of
other semiconductor and metallic nanomaterials.



FULL STORY ========================================================================== Researchers from North Carolina State University and the University
at Buffalo have developed and demonstrated a 'self-driving lab'
that uses artificial intelligence (AI) and fluidic systems to advance
our understanding of metal halide perovskite (MHP) nanocrystals. This self-driving lab can also be used to investigate a broad array of other semiconductor and metallic nanomaterials.


========================================================================== "We've created a self-driving laboratory that can be used to advance both fundamental nanoscience and applied engineering," says Milad Abolhasani, corresponding author of a paper on the work and an associate professor
of chemical and bimolecular engineering at NC State.

For their proof-of-concept demonstrations, the researchers focused on
all- inorganic metal halide perovskite (MHP) nanocrystals, cesium lead
halide (CsPbX3, X=Cl, Br). MHP nanocrystals are an emerging class of semiconductor materials that, because of their solution-processability
and unique size- and composition-tunable properties, are thought to have potential for use in printed photonic devices and energy technologies. For example, MHP nanocrystals are very efficient optically active materials
and are under consideration for use in next-generation LEDs. And because
they can be made using solution processing, they have the potential to
be made in a cost-effective way.

Solution-processed materials are materials that are made using liquid
chemical precursors, including high-value materials such as quantum dots, metal/metal oxide nanoparticles and metal organic frameworks.

However, MHP nanocrystals are not in industrial use yet.

"In part, that's because we're still developing a better understanding
of how to synthesize these nanocrystals in order to engineer all of
the properties associated with MHPs," Abolhasani says. "And, in part,
because synthesizing them requires a degree of precision that has
prevented large-scale manufacturing from being cost-effective. Our work
here addresses both of those issues." The new technology expands on
the concept of Artificial Chemist 2.0, which Abolhasani's lab unveiled
in 2020. Artificial Chemist 2.0 is completely autonomous, and uses AI
and automated robotic systems to perform multi-step chemical synthesis
and analysis. In practice, that system focused on tuning the bandgap of
MHP quantum dots, allowing users to go from requesting a custom quantum
dot to completing the relevant R&D and beginning manufacturing in less
than an hour.



==========================================================================
"Our new self-driving lab technology can autonomously dope MHP
nanocrystals, adding manganese atoms into the crystalline lattice of
the nanocrystals on demand," Abolhasani says.

Doping the material with varying levels of manganese changes the optical
and electronic properties of the nanocrystals and introduces magnetic properties to the material. For example, doping the MHP nanocrystals with manganese can change the wavelength of light emitted from the material.

"This capability gives us even greater control over the properties of the
MHP nanocrystals," Abolhasani says. "In essence, the universe of potential colors that can be produced by MHP nanocrystals is now larger. And
it's not just color. It offers a much greater range of electronic and
magnetic properties." The new self-driving lab technology also offers
a much faster and more efficient means of understanding how to engineer
MHP nanocrystals in order to obtain the desired combination of properties.

"Let's say you want to get an in-depth understanding of how
manganese-doping and bandgap tuning will affect a specific class of MHP nanocrystals, such as CsPbX3," Abolhasani says. "There are approximately
160 billionpossible experiments that you could run, if you wanted
to control for every possible variable in each experiment. Using
conventional techniques, it would still generally take hundreds
or thousands of experiments to learn how those two processes -- manganese-doping and bandgap tuning -- would affect the properties of
the cesium lead halide nanocrystals." But the new system does all of
this autonomously. Specifically, its AI algorithm selects and runs its
own experiments. The results from each completed experiment inform which experiment it will run next -- and it keeps going until it understands
which mechanisms control the MHP's various properties.



==========================================================================
"We found, in a practical demonstration, that the system was able to
get a thorough understanding of how these processes alter the properties
of cesium lead halide nanocrystals in only 60 experiments," Abolhasani
says. "In other words, we can get the information we need to engineer
a material in hours instead of months." While the work demonstrated
in the paper focuses on MHP nanocrystals, the autonomous system could
also be used to characterize other nanomaterials that are made using
solution processes, including a wide variety of metallic and semiconductor nanomaterials.

"We're excited about how this technology will broaden our understanding
of how to control the properties of these materials, but it's worth
noting that this system can also be used for continuous manufacturing," Abolhasani says. "So you can use the system to identify the best possible process for creating your desired nanocrystals, and then set the system
to start producing material nonstop -- and with incredible specificity.

"We've created a powerful technology. And we're now looking for
partners to help us apply this technology to specific challenges in
the industrial sector." The paper, "Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro- Processors," is published open access in
the journal Advanced Intelligent Systems. The paper was co-authored
by Fazel Bateni, a Ph.D. student at NC State; Robert Epps and Jeffery
Bennett, postdoctoral researchers at NC State; Kameel Antami, a former
Ph.D. student at NC State; Rokas Dargis, an undergraduate at NC State;
and Kristofer Reyes, an assistant professor at the University at Buffalo.

The work was done with support from the National Science Foundation, under grant number 1940959, and from the UNC Research Opportunities Initiative.

Video of the new technology: https://youtu.be/2BflpW6R4HI

========================================================================== Story Source: Materials provided by
North_Carolina_State_University. Original written by Matt Shipman. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Fazel Bateni, Robert W. Epps, Kameel Antami, Rokas Dargis,
Jeffery A.

Bennett, Kristofer G. Reyes, Milad Abolhasani. Autonomous
Nanocrystal Doping by Self‐Driving Fluidic
Micro‐Processors. Advanced Intelligent Systems, 2022;
2200017 DOI: 10.1002/aisy.202200017 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220316115023.htm

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