首页|Investigators at Xiangtan University Report Findings in Nanopar- ticles (Machine Learning Guided Hydrothermal Synthesis of Ther- mochromic Vo2 Nanoparticles)
Investigators at Xiangtan University Report Findings in Nanopar- ticles (Machine Learning Guided Hydrothermal Synthesis of Ther- mochromic Vo2 Nanoparticles)
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NETL
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Elsevier
2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Nanotechnology - Nanoparticles are discussed in a new report. According to news originating from Hunan, People's Republic of China, by NewsRx correspondents, research stated, "Vanadium dioxide (VO2) is a promising material for energy-saving smart windows due to its reversible metal-to-insulator transition near room temperature, concomitantly with a structural phase transition be- tween monoclinic VO2(M) phase and rutile VO2® phase. However, the fact that VO2 has a complex crystalline phase makes its reliable synthesis an obstacle to its practical application." Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province, Hunan Provincial Education Department, Hunan Provincial Innovation Foundation for Postgraduate. Our news journalists obtained a quote from the research from Xiangtan University, "Machine learning (ML), a specific subset of artificial intelligence, can be utilized to generate virtual representations of experimental conditions and outcomes for the purpose of predicting experiments. Therefore, in the paper, four machine learning models were trained to perform optimization of the VO2 hydrothermal synthesis. A random forest model achieved a classification ac-curacy of 87.27%. The synthetic parameter space was explored to filter combinations with a synthetic probability above 90%. Random forest models were used to guide the experimental synthesis, and the obtained products were characterized using X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and differential scanning calorimetry." According to the news editors, the research concluded: "The results showed that phase-pure VO2(B) and VO2(M) were successfully synthesized, demonstrating the effectiveness of machine learning in optimiz- ing material synthesis, alleviating the stochasticity of material synthesis caused by the control of synthesis conditions, and promoting the application research of VO2 materials."
HunanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNanoparticlesNanotechnologyXiangtan University
NETL
NSTL
Elsevier
