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Supercomputers Help Engineers Discover New Materials for Solar Cells and LEDs

Large-Scale Quantum Calculations are Key to Findings 


Based on a UCSD release by Liezel Labios, UCSD Jacobs School of Engineering Communications, and Kimberly Mann Bruch, SDSC Communications

One representative candidate material, (MA)2GeI4 (MA = CH3NH3), with crystal structure described by Pearson symbol tI14.


Research engineers at the University of California San Diego have developed a high-throughput computational method using XSEDE-allocated resources to design new materials for next generation solar cells and LEDs. According to their calculations, these materials, called hybrid halide semiconductors, would be stable and exhibit excellent optoelectronic properties.


The team published their findings in May in the journal 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 nano-engineering 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 began 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. Then they extracted 24 prototype structures to use as templates for generating hybrid organic-inorganic materials structures. Next, they performed high-throughput quantum mechanicsDensity Functional Theory (DFT) calculations on the prototype structures to build a comprehensive quantum materials repository containing 4,507 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 noted, adding that it took several years to develop a complete software framework equipped with data generation, data mining, and data screening algorithms for hybrid halide materials. 

Schematic illustration of the workflow for the high-throughput design of organic-inorganic hybrid halide semiconductors for solar cells and light emitting diodes.


"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 nano-engineering Ph.D. candidate in Yang's group and the first author of the study. This work could also inspire a new wave of experimental efforts to validate computationally predicted materials, Li said. 


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.


Supercomputers Power the Research

Yang attributes much of his project's success to the use of the Comet supercomputer at UC San Diego's San Diego Supercomputer Center (SDSC), which is funded by the National Science Foundation (NSF) and allocated to researchers via the NSF's Extreme Science and Engineering Discovery Environment (XSEDE) program.


"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 68 million core-hours on Comet – which made the project possible."


Having access to Comet saved more than not only saved valuable research time. "The value of these awarded computing resources is about $115,600, which also saved our project a great deal of money," said Yang.  


The study, called "High-throughput computational design of organic-inorganic hybrid halide semiconductors beyond perovskites for optoelectronics," was supported by the Global Research Outreach (GRO) Program of Samsung Advanced Institute of Technology (award number 20164974) and National Science Foundation award ACI-1550404. The use of Comet was made under XSEDE allocation DMR160045. The ab-initio molecular dynamics calculations used computational resources supplied by the Department of Defense High Performance Computing Modernization Program (HPCMP).