Vijayakumar Murugesan
Papers
3
Total Citations
87
H-Index
3
About
Vijayakumar Murugesan is a leading researcher at the intersection of energy storage and data-driven materials discovery, with a primary focus on advancing redox flow battery (RFB) technology. His work centers on solving the critical challenge of electrolyte solubility—a key determinant of energy density in RFBs—by pioneering high-throughput experimental methods and integrating them with artificial intelligence and machine learning. Murugesan’s major contributions include the development of an integrated high-throughput robotic platform combined with active learning, which dramatically accelerates the identification of optimal electrolyte formulations. His 2024 paper on this platform has already garnered 64 citations, reflecting its immediate impact on the field. Additionally, his 2023 studies on high-throughput solubility determination have laid the groundwork for data-driven materials design, amassing over 20 citations collectively. By generating extensive experimental solubility datasets—previously a major bottleneck—Murugesan has enabled more accurate AI/ML predictions, moving the field beyond trial-and-error approaches. His work is notable for bridging automation, robotics, and computational modeling, positioning him as a key figure in the next generation of battery materials discovery. For students and researchers, Murugesan exemplifies how integrating high-throughput experimentation with smart algorithms can transform energy storage research.
Research Focus
Key Achievements
Top Papers
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