Thursday, June 24, 2021

Machine Learning For New Materials Design And Performance Testing

 mit  |  Materials called perovskites are widely heralded as a likely replacement for silicon as the material of choice for solar cells, but their greatest drawback is their tendency to degrade relatively rapidly. Over recent years, the usable lifetime of perovskite-based cells has gradually improved from minutes to months, but it still lags far behind the decades expected from silicon, the material currently used for virtually all commercial solar panels.

Now, an international interdisciplinary team led by MIT has come up with a new approach to narrowing the search for the best candidates for long-lasting perovskite formulations, out of a vast number of potential combinations. Already, their system has zeroed in on one composition that in the lab has improved on existing versions more than tenfold. Even under real-world conditions at full solar cell level, beyond just a small sample in a lab, this type of perovskite has performed three times better than the state-of-the-art formulations.

The findings appear in the journal Matter, in a paper by MIT research scientist Shijing Sun, MIT professors, Moungi Bawendi,  John Fisher, and Tonio Buonassisi, who is also a principal investigator at the Singapore-MIT Alliance for Research and Technology (SMART), and 16 others from MIT, Germany, Singapore, Colorado, and New York.

Perovskites are a broad class of materials characterized by the way atoms are arranged in their layered crystal lattice. These layers, described by convention as A, B, and X, can each consist of a variety of different atoms or compounds. So, searching through the entire universe of such combinations to find the best candidates to meet specific goals — longevity, efficiency, manufacturability, and availability of source materials — is a slow and painstaking process, and largely one without any map for guidance.

“If you consider even just three elements, the most common ones in perovskites that people sub in and out are on the A site of the perovskite crystal structure,” which can each easily be varied by 1-percent increments in their relative composition, Buonassisi says. “The number of steps becomes just preposterous. It becomes very, very large” and thus impractical to search through systematically. Each step involves the complex synthesis process of creating a new material and then testing its degradation, which even under accelerated aging conditions is a time-consuming process.

The key to the team’s success is what they describe as a data fusion approach. This iterative method uses an automated system to guide the production and testing of a variety of formulations, then uses machine learning to go through the results of those tests, combined again with first-principles physical modeling, to guide the next round of experiments. The system keeps repeating that process, refining the results each time.

Buonassisi likes to compare the vast realm of possible compositions to an ocean, and he says most researchers have stayed very close to the shores of known formulations that have achieved high efficiencies, for example, by tinkering just slightly with those atomic configurations. However, “once in a while, somebody makes a mistake or has a stroke of genius and departs from that and lands somewhere else in composition space, and hey, it works better! A happy bit of serendipity, and then everybody moves over there” in their research. “But it's not usually a structured thought process.”

This new approach, he says, provides a way to explore far offshore areas in search of better properties, in a more systematic and efficient way. In their work so far, by synthesizing and testing less than 2 percent of the possible combinations among three components, the researchers were able to zero in on what seems to be the most durable formulation of a perovskite solar cell material found to date.