Researchers at the University of New South Wales have developed an AI-driven system aimed at accelerating the identification of two-dimensional semiconductor materials that can be used in solar cells.
Image: UNSW Sydney/Exciton Science
A research team at the University of New South Wales (UNSW) has developed an artificial intelligence-assisted workflow to fast-track the discovery of next-generation semiconductor materials.
“The work tackles a long-standing expensive and time-consuming problem in the field, where researchers have typically made incremental changes to known materials rather than exploring new ones systematically,” the researchers said.
The new AI-assisted workflow targets two-dimensional hybrid perovskites, a class of semiconductor materials used in solar cells and light-emitting diodes (LEDs) that are built by combining inorganic layers with organic molecules.
These organic components play a critical role in determining how the semiconductor material behaves, particularly how it transports electrical charge, but the UNSW team said designing and identifying viable materials currently relies heavily on trial-and-error and expert intuition approaches.
“Small molecular changes can radically alter performance, but with millions of possible combinations, identifying viable candidates is a major bottleneck,” the researchers said, adding that their AI-assisted approach replaces “slow, iterative materials discovery with a targeted, data-driven approach.”
Unlike previous approaches, the team’s system works backwards from a desired outcome, such as how a material should handle electrical charge, to identify molecules that could deliver that result.
Once potential candidates have been identified, the system screens large numbers of them and filters out those that would be impractical to make.
Applied across millions of molecular possibilities, the workflow was able to rapidly narrow the field to a small set of candidates that met the required criteria. These were then checked using detailed simulations to confirm their performance.
While the identified candidates have not yet been validated in the lab, the researchers said the workflow could significantly improve the efficiency of materials discovery.
“The approach could help speed up the development of new materials for electronics and energy technologies by making the search process far more efficient,” they said.
The research paper has been published in the journal Science Advances.
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