Rebecca L. Greenaway
Papers
7
Total Citations
528
H-Index
6
About
Rebecca L. Greenaway is a pioneering researcher at the intersection of computational chemistry, robotics, and supramolecular materials discovery. Her work focuses on accelerating the design and synthesis of complex organic materials — including porous organic cages, porous liquids, and catenanes — by integrating machine learning, computational prediction, and robotic automation into unified discovery workflows. Greenaway's most celebrated contribution, "High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis" (2018, 215 citations), demonstrated that vast supramolecular chemical spaces could be navigated efficiently by coupling algorithmic screening with automated experimentation — a landmark advance in the field. Building on this, her work on porous liquids (2019, 107 citations) showed how robotic platforms could rapidly map the design rules of an entirely new class of functional materials. Her 2021 paper on integrating computational and experimental workflows (75 citations) further established a generalizable framework now influencing organic materials discovery broadly. Perhaps most impressively, Greenaway has shown that complex multicomponent cage architectures can be *predicted* before they are made, as demonstrated in her self-sorting cage pots work (2019, 68 citations). Her 2023 essay "From alchemist to AI chemist" reflects her broader vision of transforming chemistry through artificial intelligence. Greenaway represents a new generation of chemists redefining how discovery itself is done.
Research Focus
Key Achievements
Top Papers
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- 2Accelerated robotic discovery of type II porous liquids107 citations · 2019
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- 6From alchemist to AI chemist22 citations · 2023
- 7Accelerated Robotic Discovery of Type II Porous Liquids6 citations · 2019