MIT, Google Brain Researchers Build Tool That Speeds Up Development of New Solar Cells

Massachusetts Institute of Technology (MIT) Researchers together with Google Brain has developed a system that helps in evaluating and predicting whether changes to material or design will provide the desired improvements and improve performance in new photovoltaic cells.

The new tool dubbed ‘differentiable solar cell simulator’ , 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,and supported in part by Eni S.p.A. and the MIT Energy Initiative, and the MIT Quest for Intelligence, was published in journal Computer Physics Communications could greatly increase the rate for the discovery of new, improved configurations.

According to research scientist, Giuseppe Romano, traditional solar cell simulators, use the details of a solar cell configuration and produce as their output a predicted efficiency, which means determining the percentage of the energy of incoming sunlight actually gets converted to an electric current. But this new simulator predicts the efficiency and indicates 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.”

One of the writers from MIT, Sean Mann believes that as traditional approaches use essentially a random search of possible variations, with this 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.

Solar cells are known to be composed of multiple layers most often interlaced with conductive materials to transmit electric charge from one to another, the new computational tool thus help reveal how the change of the thickness of the different layers will have effect on the output of the device.

Mann further explained that “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”.

The amount of doping 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 are some of the other variables to be evaluated. 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.

Mann added that the stimulator at this point is hinged on just a one-dimensional version of the solar cell, and the next step will hence 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. 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,” he noted.

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.”

Romano posited that because it 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.”

The Research team also included Eric Fadel and Steven Johnson at MIT, and Samuel Schoenholz and Ekin Cubuk at Google Brain.

Reference: “?PV: An end-to-end differentiable solar-cell simulator” by Sean Mann, Eric Fadel, Samuel S. Schoenholz, Ekin D. Cubuk, Steven G. Johnson and Giuseppe Romano, 18 November 2021, Computer Physics Communications.
DOI: 10.1016/j.cpc.2021.108232