Fred Parks
Papers
2
Total Citations
23
H-Index
2
About
Fred Parks is a pioneering researcher at the intersection of energy storage and data science, with a primary focus on advancing redox flow battery (RFB) technology through high-throughput experimentation and machine learning. His major contribution lies in addressing one of the field’s most persistent bottlenecks: the accurate prediction of electrolyte solubility, a critical parameter that directly dictates RFB energy density. Parks developed and validated a high-throughput solubility determination methodology, generating the large, high-quality datasets essential for training robust AI/ML models. This work directly tackles the “data scarcity” problem that has historically hindered data-driven materials discovery in this domain. His most-cited paper on this topic has accumulated 23 citations since 2023, reflecting its immediate impact on the community. By creating a reproducible pipeline that combines automated experimentation with computational modeling, Parks has laid the groundwork for accelerating the discovery of next-generation, highly soluble redox-active molecules. His achievements are particularly notable for bridging the gap between traditional electrochemical characterization and modern informatics, offering a scalable pathway to design better materials for grid-scale energy storage.
Research Focus
Key Achievements
Top Papers
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