Machine learning picks promising solar cell material

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.