首页|Findings from Harbin Institute of Technology Yields New Data on Machine Learning (Predicting Atomic Structure and Mechanical Properties In Quinary L12-strengthened Cobalt-based Superalloys Using Machine Learning-driven First-principles ...)
Findings from Harbin Institute of Technology Yields New Data on Machine Learning (Predicting Atomic Structure and Mechanical Properties In Quinary L12-strengthened Cobalt-based Superalloys Using Machine Learning-driven First-principles ...)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “L12-strengthened Cobalt (Co)-based superalloys are promising high-temperature materials for aero-engine applications. To make first-generation Co-Al-W-based superalloys industrially viable, it’s crucial to enhance the mechanical properties and solvus temperature of the metastable L12 phase.” Funders for this research include National Natural Science Foundation of China (NSFC), Open research fund of Songshan Lake Materials Laboratory, National Key R & D Program of China, Key-Area Research and Development Program of GuangDong Province. The news correspondents obtained a quote from the research from the Harbin Institute of Technol- ogy, “Introducing additional transition metal ™ elements into the FCC matrix is a promising strategy. Although first-principles calculations are invaluable for materials design, their high computational cost and low-efficiency for the multi-component systems, particularly those doped with TM elements, limit their practical use. In this study, we combine machine learning with first-principles calculations to accelerate the predictions of atomic structure and mechanical property. Using datasets from first-principles calculations, our ML models predict the trend in element occupancy, doping position, and mechanical attributes of the L12 phase. The ML models, further refined with first principles data, efficiently predict properties for Nb-doped systems, outperforming traditional counterparts.” According to the news reporters, the research concluded: “This methodology expedites calculations and promises advancements in designing various advanced materials, including multiple-principal-element alloys.”
ShenzhenPeople’s Republic of ChinaAsiaCobaltCyborgsEmerging TechnologiesHeavy MetalsMachine LearningTransition ElementsHarbin Institute of Tech- nology
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Elsevier
