More than 200,000 candidate materials were virtually screened by the system at Osaka University in Japan. The team of researchers then synthesized one of the most promising, and found its properties were consistent with the system’s predictions.
Machine learning allows computers to make predictions about complex situations, as long as the algorithms are supplied with sufficient example data. This is especially useful for complicated problems in material science such as designing molecules for organic solar cells, the researchers said, as it can depend on a vast array of factors and unknown molecular structures. It could take humans years to sift data to find underlying patterns, and even longer to test all the possible candidate combinations of ‘donor’ polymers and ‘acceptor’ molecules that make up organic solar cells.
The machine learning system screened the hundreds of thousands of donor-acceptor pairs, using an algorithm trained with data from previously published experimental studies. Trying all possible combinations of 382 donor molecules and 526 acceptor molecules resulted in 200,932 pairs that were virtually tested by predicting their energy conversion efficiency.
“Basing the construction of our machine learning model on an experimental dataset drastically improved the prediction accuracy,” said first author Kakaraparthi Kranthiraja.
To verify the method, one of the polymers predicted to have high efficiency was synthesized in the lab and tested. Its properties were found to conform with predictions, which gave the researchers more confidence in their approach.
“This project may contribute not only to the development of highly-efficient organic solar cells, but also [it] can be adapted to material informatics of other functional materials,” said senior author Akinori Saeki.
Similar systems could be used to screen candidates for catalysts or functional polymers, the team said.
The research was published in Cell Reports Physical Science.
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