This shows one representative candidate material – (MA)2GeI4, with a Pearson symbol tI14 – uncovered by the new computational method. Image: Yang lab.Engineers at the University of California (UC) San Diego have developed a high-throughput computational method to design new materials for next generation solar cells and LEDs. Their approach has already generated 13 new material candidates for solar cells and 23 new candidates for LEDs. Calculations predicted that these materials, called hybrid halide semiconductors, would be stable and exhibit excellent optoelectronic properties.
The team report their findings in a paper in Energy & Environmental Science.
Hybrid halide semiconductors are materials that consist of an inorganic framework housing organic cations. They show unique material properties that are not found in organic or inorganic materials alone.
A subclass of these materials, called hybrid halide perovskites, have attracted a lot of attention as promising materials for next generation solar cells and LED devices because of their exceptional optoelectronic properties and inexpensive fabrication costs. However, hybrid perovskites are not very stable and contain lead, making them unsuitable for commercial devices.
Seeking alternatives to perovskites, a team of researchers led by Kesong Yang, a nanoengineering professor at the UC San Diego Jacobs School of Engineering, used computational tools, data mining and data screening techniques to discover new hybrid halide materials beyond perovskites that are stable and lead-free. "We are looking past perovskite structures to find a new space to design hybrid semiconductor materials for optoelectronics," Yang said.
Yang's team started by going through the two largest quantum materials databases, AFLOW and The Materials Project, and analyzing all compounds that were similar in chemical composition to lead halide perovskites. They then extracted 24 prototype structures to use as templates for generating hybrid organic-inorganic materials structures.
Next, they performed high-throughput quantum mechanics calculations on the prototype structures to build a comprehensive quantum materials repository containing 4507 hypothetical hybrid halide compounds. Using efficient data mining and data screening algorithms, Yang's team rapidly identified 13 candidates for solar cell materials and 23 candidates for LEDs out of all the hypothetical compounds.
"A high-throughput study of organic-inorganic hybrid materials is not trivial," Yang said. It took several years to develop a complete software framework equipped with data generation, data mining and data screening algorithms for hybrid halide materials. It also took his team a great deal of effort to make the software framework work seamlessly with the software they used for high-throughput calculations.
"Compared to other computational design approaches, we have explored a significantly large structural and chemical space to identify novel halide semiconductor materials," said Yuheng Li, a nanoengineering PhD candidate in Yang's group and the first author of the paper. This work could also inspire a new wave of experimental efforts to the validate computationally predicted materials, Li said.
Yang attributes much of his project's success to the utilization of the Comet supercomputer at UC San Diego's San Diego Supercomputer Center (SDSC). "Our large-scale quantum mechanics calculations required a large number of computational resources," he explained. "Since 2016, we have been awarded with computing time – some 3.46 million core-hours on Comet, which made the project possible."
Moving forward, Yang and his team are using their high-throughput approach to discover new solar cell and LED materials from other types of crystal structures. They are also developing new data mining modules to discover other types of functional materials for energy conversion, optoelectronic and spintronic applications.
This story is adapted from material from the University of California San Diego, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.