Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Machine learning can help.


Date:
April 15, 2022
Source:
Massachusetts Institute of Technology
Summary:
Perovskite materials could potentially replace silicon to make solar
cells that are far thinner, lighter, and cheaper. But turning these
materials into a product that can be manufactured competitively
has been a long struggle. A new system using machine learning
could speed the development of optimized production methods,
and help make this next generation of solar power a reality.



FULL STORY ========================================================================== Perovskites are a family of materials that are currently the
leading contender to potentially replace today's silicon-based solar photovoltaics. They hold the promise of panels that are far thinner
and lighter, that could be made with ultra-high throughput at room
temperature instead of at hundreds of degrees, and that are cheaper
and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle.


========================================================================== Manufacturing perovskite-based solar cells involves optimizing at least a
dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel
approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power
a reality.

The system, developed by researchers at MIT and Stanford University
over the last few years, makes it possible to integrate data from
prior experiments, and information based on personal observations by experienced workers, into the machine learning process. This makes
the outcomes more accurate and has already led to the manufacturing of perovskite cells with an energy conversion efficiency of 18.5 percent,
a competitive level for today's market.

The research is reported in the journal Joule, in a paper by MIT
professor of mechanical engineering Tonio Buonassisi, Stanford professor
of materials science and engineering Reinhold Dauskardt, recent MIT
research assistant Zhe Liu, Stanford doctoral graduate Nicholas Rolston,
and three others.

Perovskites are a group of layered crystalline compounds defined by the configuration of the atoms in their crystal lattice. There are thousands
of such possible compounds and many different ways of making them. While
most lab- scale development of perovskite materials uses a spin-coating technique, that's not practical for larger-scale manufacturing, so
companies and labs around the world have been searching for ways of
translating these lab materials into a practical, manufacturable product.

"There's always a big challenge when you're trying to take a
lab-scale process and then transfer it to something like a startup or a manufacturing line," says Rolston, who is now an assistant professor at
Arizona State University. The team looked at a process that they felt had
the greatest potential, a method called rapid spray plasma processing,
or RSPP.



==========================================================================
The manufacturing process would involve a moving roll-to-roll surface,
or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. The
material would then move on to a curing stage, providing a rapid and
continuous output "with throughputs that are higher than for any other photovoltaic technology," Rolston says.

"The real breakthrough with this platform is that it would allow
us to scale in a way that no other material has allowed us to do,"
he adds. "Even materials like silicon require a much longer timeframe
because of the processing that's done. Whereas you can think of [this
approach as more] like spray painting." Within that process, at least a
dozen variables may affect the outcome, some of them more controllable
than others. These include the composition of the starting materials,
the temperature, the humidity, the speed of the processing path, the
distance of the nozzle used to spray the material onto a substrate, and
the methods of curing the material. Many of these factors can interact
with each other, and if the process is in open air, then humidity,
for example, may be uncontrolled. Evaluating all possible combinations
of these variables through experimentation is impossible, so machine
learning was needed to help guide the experimental process.

But while most machine-learning systems use raw data such as measurements
of the electrical and other properties of test samples, they don't
typically incorporate human experience such as qualitative observations
made by the experimenters of the visual and other properties of the
test samples, or information from other experiments reported by other researchers. So, the team found a way to incorporate such outside
information into the machine learning model, using a probability factor
based on a mathematical technique called Bayesian Optimization.

Using the system, he says, "having a model that comes from experimental
data, we can find out trends that we weren't able to see before." For
example, they initially had trouble adjusting for uncontrolled variations
in humidity in their ambient setting. But the model showed them "that
we could overcome our humidity challenges by changing the temperature,
for instance, and by changing some of the other knobs." The system now
allows experimenters to much more rapidly guide their process in order
to optimize it for a given set of conditions or required outcomes. In
their experiments, the team focused on optimizing the power output,
but the system could also be used to simultaneously incorporate other
criteria, such as cost and durability -- something members of the team
are continuing to work on, Buonassisi says.



==========================================================================
The researchers were encouraged by the Department of Energy, which
sponsored the work, to commercialize the technology, and they're currently focusing on tech transfer to existing perovskite manufacturers. "We
are reaching out to companies now," Buonassisi says, and the code
they developed has been made freely available through an open-source
server. "It's now on GitHub, anyone can download it, anyone can run it,"
he says. "We're happy to help companies get started in using our code." Already, several companies are gearing up to produce perovskite-based
solar panels, even though they are still working out the details of how
to produce them, says Liu, who is now at the Northwestern Polytechnical University in Xi'an, China. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important. Three of these companies "are on track or are being pushed
by investors to manufacture 1 meter by 2-meter rectangular modules
[comparable to today's most common solar panels], within two years,"
he says.

'The problem is, they don't have a consensus on what manufacturing
technology to use," Liu says. The RSPP method, developed at Stanford,
"still has a good chance" to be competitive, he says. And the machine
learning system the team developed could prove to be important in guiding
the optimization of whatever process ends up being used.

"The primary goal was to accelerate the process, so it required less
time, less experiments, and less human hours to develop something that
is usable right away, for free, for industry," he says.

The team also included Austin Flick and Thomas Colburn at Stanford
and Zekun Ren at the Singapore-MIT Alliance for Science and Technology
(SMART). In addition to the Department of Energy, the work was supported
by a fellowship from the MIT Energy Initiative, the Graduate Research Fellowship Program from the National Science Foundation, and the SMART
program.


========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by David
L. Chandler. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Zhe Liu, Nicholas Rolston, Austin C. Flick, Thomas W. Colburn,
Zekun Ren,
Reinhold H. Dauskardt, Tonio Buonassisi. Machine learning with
knowledge constraints for process optimization of open-air
perovskite solar cell manufacturing. Joule, 2022; DOI:
10.1016/j.joule.2022.03.003 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220415135408.htm

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