首页|Data from University of Southern California (USC) Provide New Insights into Mach ine Learning (Compression Eliminates Charge Traps By Stabilizing Perovskite Grai n Boundary Structures: an Ab Initio Analysis With Machine Learning Force Field)
Data from University of Southern California (USC) Provide New Insights into Mach ine Learning (Compression Eliminates Charge Traps By Stabilizing Perovskite Grai n Boundary Structures: an Ab Initio Analysis With Machine Learning Force Field)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news reportingoriginating in Los Angeles, Califor nia, by NewsRx journalists, research stated, “Grain boundaries (GBs)play an imp ortant role in determining the optoelectronic properties of perovskites, requiri ng an atomisticunderstanding of the underlying mechanisms. Strain engineering h as recently been employed in perovskitesolar cells, providing a novel perspecti ve on the role of perovskite GBs.”
Los AngelesCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUni versity of Southern California (USC)
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