Researchers have developed a computational simulator that can help predict whether changes to materials or design will improve performance in new photovoltaic cells.

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.

Story Source: Materials provided by Massachusetts Institute of Technology. Original written by David L. Chandler. Note: Content may be edited for style and length.