首页|Southern University of Science and Technology (SUSTech) Reports Findings in Mach ine Learning (High-Throughput Computational Study and Machine Learning Predictio n of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional .. .)
Southern University of Science and Technology (SUSTech) Reports Findings in Mach ine Learning (High-Throughput Computational Study and Machine Learning Predictio n of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional .. .)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Shenzhen, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Heterostructures for med by transition metal dichalcogenides (TMDs) and two-dimensional layered halid e perovskites (2D-LHPs) have attracted significant attention due to their unique optoelectronic properties. However, theoretical studies face challenges due to the large number of atoms and the need for lattice matching.” Our news journalists obtained a quote from the research from the Southern Univer sity of Science and Technology (SUSTech), “With the discovery of more 2D-LHPs, t here is an urgent need for methods to rapidly predict and screen TMDs/2D-LHPs he terostructures. This study employs first-principles calculations to perform high -throughput computations on 602 TMDs/2D-LHPs heterostructures. Results show that different combinations exhibit diverse band alignments, with MoS and WS more li kely to form type- II heterostructures with 2D-LHPs. The highest photoelectric co nversion efficiency of type-II structures reaches 23.26%, demonstra ting potential applications in solar cells. Notably, some MoS/2D-LHPs form type- S structures, showing promise in photocatalysis. Furthermore, we found that TMDs can significantly affect the conformation of organic molecules in 2D-LHPs, thus modulating the electronic properties of the heterostructures. To overcome compu tational cost limitations, we constructed a crystal graph convolutional neural n etwork model based on the calculated data to predict the electronic properties o f TMDs/2D-LHPs heterostructures.”
ShenzhenPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine Learning
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