Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory and Purdue University have developed a machine learning method for screening many thousands of compounds as solar absorbers. Argonne's Maria Chan and Purdue's Arun Mannodi-Kanakkithodi, who led the study, chose to work with a form of artificial intelligence (AI) that uses a combination of large data sets and algorithms to imitate the way that humans learn. It learns from training with sample data and past experience to make ever better predictions.
The team used their machine learning method to assess the solar energy properties of halide perovskites. "Unlike silicon or cadmium telluride, the possible variations of halides combined with perovskites are essentially unlimited," said Chan. "There is thus an urgent need to develop a method that can narrow the promising candidates to a manageable number. To that end, machine learning is a perfect tool."
The team trained their method with data for a few hundred halide perovskite compositions, then applied it to over 18,000 compositions as a test case. The method evaluated these compositions for key properties such as stability, ability to absorb sunlight, structure that does not break easily due to defects, and more. The computations agreed well with relevant data in the scientific literature. Also, the findings whittled down the number of compositions worthy of further study to about 400.
"Our list of candidates has compounds that have already been studied, compounds that no one has ever studied, and even compounds that were not among the original 18,000," said Chan. "So we are very excited about that."
The next step will be to test the predictions using experiments. The ideal scenario would be to use an autonomous discovery laboratory, such as Polybot at Argonne's Center for Nanoscale Materials (CNM), a DOE Office of Science user facility. Polybot brings together the power of robotics with AI to drive scientific discovery with little or no human intervention.
By using autonomous experimentation to synthesize, characterize and test the best of their few hundred prime candidates, Chan and her team anticipate they can also improve the current machine learning method.
"We are truly in a new era of applying AI and high performance computing to materials discovery," said Chan. "Besides solar cells, our design methodology could apply to LEDs and infrared sensors."
