A tool to speed development of new solar cells

Date:
December 9, 2021
Source:
Massachusetts Institute of Technology
Summary:
Researchers have developed a computational simulator that can
help predict whether changes to materials or design will improve
performance in new photovoltaic cells.



FULL STORY ==========================================================================
In the ongoing race to develop ever-better materials and configurations
for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement.

Developing new solar cells has generally been a tedious process of making
small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without
having to actually build each new variation for testing, the process
remains slow.


==========================================================================
Now, researchers at MIT and Google Brain have developed a system that
makes it possible not just to evaluate one proposed design at a time,
but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of
new, improved configurations.

The new system, called a differentiable solar cell simulator, is described
in a paper published in the journal Computer Physics Communications,
written by MIT junior Sean Mann, research scientist Giuseppe Romano of
MIT's Institute for Soldier Nanotechnologies, and four others at MIT
and at Google Brain.

Traditional solar cell simulators, Romano explains, take the details
of a solar cell configuration and produce as their output a predicted efficiency -- that is, what percentage of the energy of incoming sunlight actually gets converted to an electric current. But this new simulator
both predicts the efficiency and shows how much that output is affected
by any one of the input parameters. "It tells you directly what happens
to the efficiency if we make this layer a little bit thicker, or what
happens to the efficiency if we for example change the property of the material," he says.

In short, he says, "we didn't discover a new device, but we developed
a tool that will enable others to discover more quickly other higher performance devices." Using this system, "we are decreasing the number
of times that we need to run a simulator to give quicker access to a
wider space of optimized structures." In addition, he says, "our tool can identify a unique set of material parameters that has been hidden so far because it's very complex to run those simulations." While traditional approaches use essentially a random search of possible variations,
Mann says, with his tool "we can follow a trajectory of change because
the simulator tells you what direction you want to be changing your
device. That makes the process much faster because instead of exploring
the entire space of opportunities, you can just follow a single path"
that leads directly to improved performance.



========================================================================== Since advanced solar cells often are composed of multiple layers
interlaced with conductive materials to carry electric charge from
one to the other, this computational tool reveals how changing the
relative thicknesses of these different layers will affect the device's
output. "This is very important because the thickness is critical. There
is a strong interplay between light propagation and the thickness of
each layer and the absorption of each layer," Mann explains.

Other variables that can be evaluated include the amount of doping (the introduction of atoms of another element) that each layer receives,
or the dielectric constant of insulating layers, or the bandgap, a
measure of the energy levels of photons of light that can be captured
by different materials used in the layers.

This simulator is now available as an open-source tool that can be used immediately to help guide research in this field, Romano says. "It is
ready, and can be taken up by industry experts." To make use of it,
researchers would couple this device's computations with an optimization algorithm, or even a machine learning system, to rapidly assess a wide
variety of possible changes and home in quickly on the most promising alternatives.

At this point, the simulator is based on just a one-dimensional version
of the solar cell, so the next step will be to expand its capabilities
to include two- and three-dimensional configurations. But even this
1D version "can cover the majority of cells that are currently under production," Romano says. Certain variations, such as so-called tandem
cells using different materials, cannot yet be simulated directly by
this tool, but "there are ways to approximate a tandem solar cell by
simulating each of the individual cells," Mann says.

The simulator is "end-to-end," Romano says, meaning it computes
the sensitivity of the efficiency, also taking into account light
absorption. He adds: "An appealing future direction is composing
our simulator with advanced existing differentiable light-propagation simulators, to achieve enhanced accuracy." Moving forward, Romano says, because this is an open-source code, "that means that once it's up
there, the community can contribute to it. And that's why we are really excited." Although this research group is "just a handful of people,"
he says, now anyone working in the field can make their own enhancements
and improvements to the code and introduce new capabilities.

"Differentiable physics is going to provide new capabilities for
the simulations of engineered systems," says Venkat Viswanathan,
an associate professor of mechanical engineering at Carnegie Mellon
University, who was not associated with this work. "The differentiable
solar cell simulator is an incredible example of differentiable physics,
that can now provide new capabilities to optimize solar cell device performance," he says, calling the study "an exciting step forward."
In addition to Mann and Romano, the team included Eric Fadel and Steven
Johnson at MIT, and Samuel Schoenholz and Ekin Cubuk at Google Brain. The
work was supported in part by Eni S.p.A. and the MIT Energy Initiative,
and the MIT Quest for Intelligence.

========================================================================== 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. Sean Mann, Eric Fadel, Samuel S. Schoenholz, Ekin D. Cubuk,
Steven G.

Johnson, Giuseppe Romano. PARTPV: An end-to-end differentiable
solar-cell simulator. Computer Physics Communications, 2022; 272:
108232 DOI: 10.1016/j.cpc.2021.108232 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211209124258.htm

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